[{"data":1,"prerenderedAt":2208},["ShallowReactive",2],{"page-\u002Fprompt-engineering\u002F12-retrieval-augmented-generation-rag":3},{"id":4,"title":5,"body":6,"description":2201,"extension":2202,"meta":2203,"navigation":82,"path":2204,"seo":2205,"stem":2206,"__hash__":2207},"content\u002Fprompt-engineering\u002F12-retrieval-augmented-generation-rag.md","12 — Retrieval-Augmented Generation (RAG)",{"type":7,"value":8,"toc":2187},"minimark",[9,13,18,107,111,202,206,245,301,305,309,385,876,880,954,962,966,999,1096,1100,1533,1537,1769,1773,1887,1891,2025,2029,2036,2063,2067,2183],[10,11,5],"h1",{"id":12},"_12-retrieval-augmented-generation-rag",[14,15,17],"h2",{"id":16},"the-problem-rag-solves","The Problem RAG Solves",[19,20,23],"code-wrapper",{"filename":21,"language":22},"rag_problem.py","python",[24,25,29],"pre",{"className":26,"code":27,"language":22,"meta":28,"style":28},"language-python shiki shiki-themes github-light github-dark","# A model's training data has a cutoff date, doesn't include your company's\n# internal documents, and recalls facts PROBABILISTICALLY rather than by lookup.\n# It can be wrong with the SAME fluent confidence as when it's right.\n#\n# None of these are bugs to prompt around — they're structural properties of\n# a pretrained model. If you need answers grounded in specific, current, or\n# private information, you must GIVE the model that information at inference time.\n\n# RAG = retrieve relevant documents → insert into prompt → model answers\n# from the provided context instead of parametric memory.\n#\n# \"Does the model know this?\" → \"here IS the information; use it.\"\n","",[30,31,32,41,47,53,59,65,71,77,84,90,96,101],"code",{"__ignoreMap":28},[33,34,37],"span",{"class":35,"line":36},"line",1,[33,38,40],{"class":39},"sdCPZ","# A model's training data has a cutoff date, doesn't include your company's\n",[33,42,44],{"class":35,"line":43},2,[33,45,46],{"class":39},"# internal documents, and recalls facts PROBABILISTICALLY rather than by lookup.\n",[33,48,50],{"class":35,"line":49},3,[33,51,52],{"class":39},"# It can be wrong with the SAME fluent confidence as when it's right.\n",[33,54,56],{"class":35,"line":55},4,[33,57,58],{"class":39},"#\n",[33,60,62],{"class":35,"line":61},5,[33,63,64],{"class":39},"# None of these are bugs to prompt around — they're structural properties of\n",[33,66,68],{"class":35,"line":67},6,[33,69,70],{"class":39},"# a pretrained model. If you need answers grounded in specific, current, or\n",[33,72,74],{"class":35,"line":73},7,[33,75,76],{"class":39},"# private information, you must GIVE the model that information at inference time.\n",[33,78,80],{"class":35,"line":79},8,[33,81,83],{"emptyLinePlaceholder":82},true,"\n",[33,85,87],{"class":35,"line":86},9,[33,88,89],{"class":39},"# RAG = retrieve relevant documents → insert into prompt → model answers\n",[33,91,93],{"class":35,"line":92},10,[33,94,95],{"class":39},"# from the provided context instead of parametric memory.\n",[33,97,99],{"class":35,"line":98},11,[33,100,58],{"class":39},[33,102,104],{"class":35,"line":103},12,[33,105,106],{"class":39},"# \"Does the model know this?\" → \"here IS the information; use it.\"\n",[14,108,110],{"id":109},"the-basic-shape","The Basic Shape",[19,112,115],{"filename":113,"language":114},"rag_basic.md","markdown",[24,116,119],{"className":117,"code":118,"language":114,"meta":28,"style":28},"language-markdown shiki shiki-themes github-light github-dark","Answer the user's question using only the information in the provided\ndocuments below. If the documents don't contain enough information to answer\nconfidently, say so explicitly rather than guessing.\n\nDocuments:\n\u003Cdocument id=\"1\" source=\"employee-handbook-2026.pdf\" page=\"14\">\nEmployees are entitled to 15 days of paid time off per year, accruing\nat 1.25 days per month, starting from their first day of employment.\n\u003C\u002Fdocument>\n\n\u003Cdocument id=\"2\" source=\"employee-handbook-2026.pdf\" page=\"15\">\nUnused PTO up to 5 days may be carried over into the following calendar\nyear. Any remaining unused balance beyond 5 days is forfeited.\n\u003C\u002Fdocument>\n\nQuestion: How much PTO can I roll over to next year?\n",[30,120,121,127,132,137,141,146,151,156,161,166,170,175,180,186,191,196],{"__ignoreMap":28},[33,122,123],{"class":35,"line":36},[33,124,126],{"class":125},"ssxIu","Answer the user's question using only the information in the provided\n",[33,128,129],{"class":35,"line":43},[33,130,131],{"class":125},"documents below. If the documents don't contain enough information to answer\n",[33,133,134],{"class":35,"line":49},[33,135,136],{"class":125},"confidently, say so explicitly rather than guessing.\n",[33,138,139],{"class":35,"line":55},[33,140,83],{"emptyLinePlaceholder":82},[33,142,143],{"class":35,"line":61},[33,144,145],{"class":125},"Documents:\n",[33,147,148],{"class":35,"line":67},[33,149,150],{"class":125},"\u003Cdocument id=\"1\" source=\"employee-handbook-2026.pdf\" page=\"14\">\n",[33,152,153],{"class":35,"line":73},[33,154,155],{"class":125},"Employees are entitled to 15 days of paid time off per year, accruing\n",[33,157,158],{"class":35,"line":79},[33,159,160],{"class":125},"at 1.25 days per month, starting from their first day of employment.\n",[33,162,163],{"class":35,"line":86},[33,164,165],{"class":125},"\u003C\u002Fdocument>\n",[33,167,168],{"class":35,"line":92},[33,169,83],{"emptyLinePlaceholder":82},[33,171,172],{"class":35,"line":98},[33,173,174],{"class":125},"\u003Cdocument id=\"2\" source=\"employee-handbook-2026.pdf\" page=\"15\">\n",[33,176,177],{"class":35,"line":103},[33,178,179],{"class":125},"Unused PTO up to 5 days may be carried over into the following calendar\n",[33,181,183],{"class":35,"line":182},13,[33,184,185],{"class":125},"year. Any remaining unused balance beyond 5 days is forfeited.\n",[33,187,189],{"class":35,"line":188},14,[33,190,165],{"class":125},[33,192,194],{"class":35,"line":193},15,[33,195,83],{"emptyLinePlaceholder":82},[33,197,199],{"class":35,"line":198},16,[33,200,201],{"class":125},"Question: How much PTO can I roll over to next year?\n",[14,203,205],{"id":204},"anti-pattern-no-grounding-instruction","Anti-Pattern: No Grounding Instruction",[19,207,209],{"filename":208,"language":114},"anti_pattern_no_grounding.md",[24,210,212],{"className":117,"code":211,"language":114,"meta":28,"style":28},"\u003C!-- ANTI-PATTERN: documents are \"provided\" but nothing stops the model from\n     blending in its own pretrained knowledge -->\nHere are some documents about our return policy: [documents]\n\nWhat's our return policy?\n",[30,213,214,219,224,236,240],{"__ignoreMap":28},[33,215,216],{"class":35,"line":36},[33,217,218],{"class":39},"\u003C!-- ANTI-PATTERN: documents are \"provided\" but nothing stops the model from\n",[33,220,221],{"class":35,"line":43},[33,222,223],{"class":39},"     blending in its own pretrained knowledge -->\n",[33,225,226,229,233],{"class":35,"line":49},[33,227,228],{"class":125},"Here are some documents about our return policy: [",[33,230,232],{"class":231},"sSQSC","documents",[33,234,235],{"class":125},"]\n",[33,237,238],{"class":35,"line":55},[33,239,83],{"emptyLinePlaceholder":82},[33,241,242],{"class":35,"line":61},[33,243,244],{"class":125},"What's our return policy?\n",[19,246,248],{"filename":247,"language":114},"production_grounding.md",[24,249,251],{"className":117,"code":250,"language":114,"meta":28,"style":28},"\u003C!-- PRODUCTION: the single highest-leverage sentence in most RAG prompts -->\nAnswer using ONLY the information in the documents below. Do not use any\noutside knowledge, even if you believe you know the answer. If the\ndocuments do not fully answer the question, state exactly what\ninformation is missing rather than filling the gap with an assumption.\n\nDocuments: [...]\n\nQuestion: What's our return policy for items purchased on sale?\n",[30,252,253,258,263,268,273,278,282,292,296],{"__ignoreMap":28},[33,254,255],{"class":35,"line":36},[33,256,257],{"class":39},"\u003C!-- PRODUCTION: the single highest-leverage sentence in most RAG prompts -->\n",[33,259,260],{"class":35,"line":43},[33,261,262],{"class":125},"Answer using ONLY the information in the documents below. Do not use any\n",[33,264,265],{"class":35,"line":49},[33,266,267],{"class":125},"outside knowledge, even if you believe you know the answer. If the\n",[33,269,270],{"class":35,"line":55},[33,271,272],{"class":125},"documents do not fully answer the question, state exactly what\n",[33,274,275],{"class":35,"line":61},[33,276,277],{"class":125},"information is missing rather than filling the gap with an assumption.\n",[33,279,280],{"class":35,"line":67},[33,281,83],{"emptyLinePlaceholder":82},[33,283,284,287,290],{"class":35,"line":73},[33,285,286],{"class":125},"Documents: [",[33,288,289],{"class":231},"...",[33,291,235],{"class":125},[33,293,294],{"class":35,"line":79},[33,295,83],{"emptyLinePlaceholder":82},[33,297,298],{"class":35,"line":86},[33,299,300],{"class":125},"Question: What's our return policy for items purchased on sale?\n",[302,303,304],"p",{},"Without the explicit grounding instruction, the model silently blends retrieved facts with pretrained \"knowledge\" — and the blend is indistinguishable to the reader. The fabricated part looks exactly as confident as the grounded part.",[14,306,308],{"id":307},"citation-requirements","Citation Requirements",[19,310,312],{"filename":311,"language":114},"rag_citation.md",[24,313,315],{"className":117,"code":314,"language":114,"meta":28,"style":28},"Answer the question using the documents below. After each claim in your\nanswer, cite the supporting document using its id in brackets, like [1].\nIf a sentence draws on multiple documents, cite all of them, like [1][3].\nDo not make any claim that isn't traceable to at least one cited document.\n\n\u003Cdocument id=\"1\">...\u003C\u002Fdocument>\n\u003Cdocument id=\"2\">...\u003C\u002Fdocument>\n\u003Cdocument id=\"3\">...\u003C\u002Fdocument>\n\nQuestion: [...]\n",[30,316,317,322,333,348,353,357,362,367,372,376],{"__ignoreMap":28},[33,318,319],{"class":35,"line":36},[33,320,321],{"class":125},"Answer the question using the documents below. After each claim in your\n",[33,323,324,327,330],{"class":35,"line":43},[33,325,326],{"class":125},"answer, cite the supporting document using its id in brackets, like [",[33,328,329],{"class":231},"1",[33,331,332],{"class":125},"].\n",[33,334,335,338,340,343,346],{"class":35,"line":49},[33,336,337],{"class":125},"If a sentence draws on multiple documents, cite all of them, like [",[33,339,329],{"class":231},[33,341,342],{"class":125},"][",[33,344,345],{"class":231},"3",[33,347,332],{"class":125},[33,349,350],{"class":35,"line":55},[33,351,352],{"class":125},"Do not make any claim that isn't traceable to at least one cited document.\n",[33,354,355],{"class":35,"line":61},[33,356,83],{"emptyLinePlaceholder":82},[33,358,359],{"class":35,"line":67},[33,360,361],{"class":125},"\u003Cdocument id=\"1\">...\u003C\u002Fdocument>\n",[33,363,364],{"class":35,"line":73},[33,365,366],{"class":125},"\u003Cdocument id=\"2\">...\u003C\u002Fdocument>\n",[33,368,369],{"class":35,"line":79},[33,370,371],{"class":125},"\u003Cdocument id=\"3\">...\u003C\u002Fdocument>\n",[33,373,374],{"class":35,"line":86},[33,375,83],{"emptyLinePlaceholder":82},[33,377,378,381,383],{"class":35,"line":92},[33,379,380],{"class":125},"Question: [",[33,382,289],{"class":231},[33,384,235],{"class":125},[19,386,388],{"filename":387,"language":22},"citation_check.py",[24,389,391],{"className":26,"code":390,"language":22,"meta":28,"style":28},"# Citations give you a MECHANICAL way to check groundedness programmatically.\n# If the model cites document 2 for a claim, your code can check whether that\n# claim's content actually appears in document 2's text.\n\nimport re\n\ndef verify_citations(answer: str, documents: dict[str, str]) -> list[dict]:\n    \"\"\"Check that each cited claim is traceable to its cited document.\"\"\"\n    issues = []\n\n    # Split answer into sentences with citations\n    sentences = re.findall(r'(.+?)\\[(\\d+(?:\\]\\[\\d+)*)\\]\\.?', answer)\n\n    for sentence, citations in sentences:\n        cited_ids = re.findall(r'\\d+', citations)\n        for doc_id in cited_ids:\n            if doc_id not in documents:\n                issues.append({\"type\": \"missing_doc\", \"citation\": doc_id, \"sentence\": sentence})\n                continue\n\n            # Heuristic: check if key words from the sentence appear in the cited doc\n            # (A full check would use an LLM or embedding similarity)\n            doc_text = documents[doc_id].lower()\n            sentence_words = set(re.findall(r'\\b[a-z]{4,}\\b', sentence.lower()))\n            doc_words = set(re.findall(r'\\b[a-z]{4,}\\b', doc_text))\n            overlap = sentence_words & doc_words\n\n            if len(overlap) \u003C 2:  # very low overlap = suspicious\n                issues.append({\n                    \"type\": \"weak_support\",\n                    \"citation\": doc_id,\n                    \"sentence\": sentence,\n                    \"overlap_words\": list(overlap),\n                })\n\n    return issues\n\n# An uncited claim in a RAG system is functionally the same as an ungrounded one,\n# even if it happens to be correct. A citation is only as trustworthy as it is CHECKABLE.\n",[30,392,393,398,403,408,412,421,425,460,466,477,481,486,550,554,568,590,603,620,649,655,660,666,672,683,715,742,759,764,787,793,807,816,825,839,845,850,859,864,870],{"__ignoreMap":28},[33,394,395],{"class":35,"line":36},[33,396,397],{"class":39},"# Citations give you a MECHANICAL way to check groundedness programmatically.\n",[33,399,400],{"class":35,"line":43},[33,401,402],{"class":39},"# If the model cites document 2 for a claim, your code can check whether that\n",[33,404,405],{"class":35,"line":49},[33,406,407],{"class":39},"# claim's content actually appears in document 2's text.\n",[33,409,410],{"class":35,"line":55},[33,411,83],{"emptyLinePlaceholder":82},[33,413,414,418],{"class":35,"line":61},[33,415,417],{"class":416},"svdQ7","import",[33,419,420],{"class":125}," re\n",[33,422,423],{"class":35,"line":67},[33,424,83],{"emptyLinePlaceholder":82},[33,426,427,430,434,437,441,444,446,449,451,454,457],{"class":35,"line":73},[33,428,429],{"class":416},"def",[33,431,433],{"class":432},"sIsaT"," verify_citations",[33,435,436],{"class":125},"(answer: ",[33,438,440],{"class":439},"snvgF","str",[33,442,443],{"class":125},", documents: dict[",[33,445,440],{"class":439},[33,447,448],{"class":125},", ",[33,450,440],{"class":439},[33,452,453],{"class":125},"]) -> list[",[33,455,456],{"class":439},"dict",[33,458,459],{"class":125},"]:\n",[33,461,462],{"class":35,"line":79},[33,463,465],{"class":464},"sJ6F3","    \"\"\"Check that each cited claim is traceable to its cited document.\"\"\"\n",[33,467,468,471,474],{"class":35,"line":86},[33,469,470],{"class":125},"    issues ",[33,472,473],{"class":416},"=",[33,475,476],{"class":125}," []\n",[33,478,479],{"class":35,"line":92},[33,480,83],{"emptyLinePlaceholder":82},[33,482,483],{"class":35,"line":98},[33,484,485],{"class":39},"    # Split answer into sentences with citations\n",[33,487,488,491,493,496,499,502,505,508,511,515,518,521,524,527,530,532,534,537,539,542,545,547],{"class":35,"line":103},[33,489,490],{"class":125},"    sentences ",[33,492,473],{"class":416},[33,494,495],{"class":125}," re.findall(",[33,497,498],{"class":416},"r",[33,500,501],{"class":464},"'",[33,503,504],{"class":439},"(.",[33,506,507],{"class":416},"+?",[33,509,510],{"class":439},")",[33,512,514],{"class":513},"snRuI","\\[",[33,516,517],{"class":439},"(\\d",[33,519,520],{"class":416},"+",[33,522,523],{"class":439},"(?:",[33,525,526],{"class":513},"\\]\\[",[33,528,529],{"class":439},"\\d",[33,531,520],{"class":416},[33,533,510],{"class":439},[33,535,536],{"class":416},"*",[33,538,510],{"class":439},[33,540,541],{"class":513},"\\]\\.",[33,543,544],{"class":416},"?",[33,546,501],{"class":464},[33,548,549],{"class":125},", answer)\n",[33,551,552],{"class":35,"line":182},[33,553,83],{"emptyLinePlaceholder":82},[33,555,556,559,562,565],{"class":35,"line":188},[33,557,558],{"class":416},"    for",[33,560,561],{"class":125}," sentence, citations ",[33,563,564],{"class":416},"in",[33,566,567],{"class":125}," sentences:\n",[33,569,570,573,575,577,579,581,583,585,587],{"class":35,"line":193},[33,571,572],{"class":125},"        cited_ids ",[33,574,473],{"class":416},[33,576,495],{"class":125},[33,578,498],{"class":416},[33,580,501],{"class":464},[33,582,529],{"class":439},[33,584,520],{"class":416},[33,586,501],{"class":464},[33,588,589],{"class":125},", citations)\n",[33,591,592,595,598,600],{"class":35,"line":198},[33,593,594],{"class":416},"        for",[33,596,597],{"class":125}," doc_id ",[33,599,564],{"class":416},[33,601,602],{"class":125}," cited_ids:\n",[33,604,606,609,611,614,617],{"class":35,"line":605},17,[33,607,608],{"class":416},"            if",[33,610,597],{"class":125},[33,612,613],{"class":416},"not",[33,615,616],{"class":416}," in",[33,618,619],{"class":125}," documents:\n",[33,621,623,626,629,632,635,637,640,643,646],{"class":35,"line":622},18,[33,624,625],{"class":125},"                issues.append({",[33,627,628],{"class":464},"\"type\"",[33,630,631],{"class":125},": ",[33,633,634],{"class":464},"\"missing_doc\"",[33,636,448],{"class":125},[33,638,639],{"class":464},"\"citation\"",[33,641,642],{"class":125},": doc_id, ",[33,644,645],{"class":464},"\"sentence\"",[33,647,648],{"class":125},": sentence})\n",[33,650,652],{"class":35,"line":651},19,[33,653,654],{"class":416},"                continue\n",[33,656,658],{"class":35,"line":657},20,[33,659,83],{"emptyLinePlaceholder":82},[33,661,663],{"class":35,"line":662},21,[33,664,665],{"class":39},"            # Heuristic: check if key words from the sentence appear in the cited doc\n",[33,667,669],{"class":35,"line":668},22,[33,670,671],{"class":39},"            # (A full check would use an LLM or embedding similarity)\n",[33,673,675,678,680],{"class":35,"line":674},23,[33,676,677],{"class":125},"            doc_text ",[33,679,473],{"class":416},[33,681,682],{"class":125}," documents[doc_id].lower()\n",[33,684,686,689,691,694,697,699,701,704,707,710,712],{"class":35,"line":685},24,[33,687,688],{"class":125},"            sentence_words ",[33,690,473],{"class":416},[33,692,693],{"class":439}," set",[33,695,696],{"class":125},"(re.findall(",[33,698,498],{"class":416},[33,700,501],{"class":464},[33,702,703],{"class":439},"\\b[a-z]",[33,705,706],{"class":416},"{4,}",[33,708,709],{"class":439},"\\b",[33,711,501],{"class":464},[33,713,714],{"class":125},", sentence.lower()))\n",[33,716,718,721,723,725,727,729,731,733,735,737,739],{"class":35,"line":717},25,[33,719,720],{"class":125},"            doc_words ",[33,722,473],{"class":416},[33,724,693],{"class":439},[33,726,696],{"class":125},[33,728,498],{"class":416},[33,730,501],{"class":464},[33,732,703],{"class":439},[33,734,706],{"class":416},[33,736,709],{"class":439},[33,738,501],{"class":464},[33,740,741],{"class":125},", doc_text))\n",[33,743,745,748,750,753,756],{"class":35,"line":744},26,[33,746,747],{"class":125},"            overlap ",[33,749,473],{"class":416},[33,751,752],{"class":125}," sentence_words ",[33,754,755],{"class":416},"&",[33,757,758],{"class":125}," doc_words\n",[33,760,762],{"class":35,"line":761},27,[33,763,83],{"emptyLinePlaceholder":82},[33,765,767,769,772,775,778,781,784],{"class":35,"line":766},28,[33,768,608],{"class":416},[33,770,771],{"class":439}," len",[33,773,774],{"class":125},"(overlap) ",[33,776,777],{"class":416},"\u003C",[33,779,780],{"class":439}," 2",[33,782,783],{"class":125},":  ",[33,785,786],{"class":39},"# very low overlap = suspicious\n",[33,788,790],{"class":35,"line":789},29,[33,791,792],{"class":125},"                issues.append({\n",[33,794,796,799,801,804],{"class":35,"line":795},30,[33,797,798],{"class":464},"                    \"type\"",[33,800,631],{"class":125},[33,802,803],{"class":464},"\"weak_support\"",[33,805,806],{"class":125},",\n",[33,808,810,813],{"class":35,"line":809},31,[33,811,812],{"class":464},"                    \"citation\"",[33,814,815],{"class":125},": doc_id,\n",[33,817,819,822],{"class":35,"line":818},32,[33,820,821],{"class":464},"                    \"sentence\"",[33,823,824],{"class":125},": sentence,\n",[33,826,828,831,833,836],{"class":35,"line":827},33,[33,829,830],{"class":464},"                    \"overlap_words\"",[33,832,631],{"class":125},[33,834,835],{"class":439},"list",[33,837,838],{"class":125},"(overlap),\n",[33,840,842],{"class":35,"line":841},34,[33,843,844],{"class":125},"                })\n",[33,846,848],{"class":35,"line":847},35,[33,849,83],{"emptyLinePlaceholder":82},[33,851,853,856],{"class":35,"line":852},36,[33,854,855],{"class":416},"    return",[33,857,858],{"class":125}," issues\n",[33,860,862],{"class":35,"line":861},37,[33,863,83],{"emptyLinePlaceholder":82},[33,865,867],{"class":35,"line":866},38,[33,868,869],{"class":39},"# An uncited claim in a RAG system is functionally the same as an ungrounded one,\n",[33,871,873],{"class":35,"line":872},39,[33,874,875],{"class":39},"# even if it happens to be correct. A citation is only as trustworthy as it is CHECKABLE.\n",[14,877,879],{"id":878},"handling-contradictions-in-retrieved-context","Handling Contradictions in Retrieved Context",[19,881,883],{"filename":882,"language":114},"contradiction_handling.md",[24,884,886],{"className":117,"code":885,"language":114,"meta":28,"style":28},"The documents below may contain conflicting information (e.g., an\noutdated policy alongside a current one). If you find a direct\ncontradiction between documents on the specific question asked:\n\n1. Prefer the document with the more recent date if dates are available.\n2. If no date distinguishes them, or both are equally current, do not\n   silently pick one — explicitly tell the user both versions exist and\n   quote each, so they can determine which applies to their situation.\n\nDocuments: [...]\nQuestion: [...]\n",[30,887,888,893,898,903,907,916,924,929,934,938,946],{"__ignoreMap":28},[33,889,890],{"class":35,"line":36},[33,891,892],{"class":125},"The documents below may contain conflicting information (e.g., an\n",[33,894,895],{"class":35,"line":43},[33,896,897],{"class":125},"outdated policy alongside a current one). If you find a direct\n",[33,899,900],{"class":35,"line":49},[33,901,902],{"class":125},"contradiction between documents on the specific question asked:\n",[33,904,905],{"class":35,"line":55},[33,906,83],{"emptyLinePlaceholder":82},[33,908,909,913],{"class":35,"line":61},[33,910,912],{"class":911},"sCrzJ","1.",[33,914,915],{"class":125}," Prefer the document with the more recent date if dates are available.\n",[33,917,918,921],{"class":35,"line":67},[33,919,920],{"class":911},"2.",[33,922,923],{"class":125}," If no date distinguishes them, or both are equally current, do not\n",[33,925,926],{"class":35,"line":73},[33,927,928],{"class":125},"   silently pick one — explicitly tell the user both versions exist and\n",[33,930,931],{"class":35,"line":79},[33,932,933],{"class":125},"   quote each, so they can determine which applies to their situation.\n",[33,935,936],{"class":35,"line":86},[33,937,83],{"emptyLinePlaceholder":82},[33,939,940,942,944],{"class":35,"line":92},[33,941,286],{"class":125},[33,943,289],{"class":231},[33,945,235],{"class":125},[33,947,948,950,952],{"class":35,"line":98},[33,949,380],{"class":125},[33,951,289],{"class":231},[33,953,235],{"class":125},[302,955,956,957,961],{},"This is also an argument for including document ",[958,959,960],"strong",{},"metadata"," (source, date, version) in the retrieved context — the model can't prefer the more recent source if the date was never given to it.",[14,963,965],{"id":964},"handling-empty-or-irrelevant-retrieval","Handling Empty or Irrelevant Retrieval",[19,967,969],{"filename":968,"language":114},"empty_retrieval.md",[24,970,972],{"className":117,"code":971,"language":114,"meta":28,"style":28},"Answer using ONLY the documents below. If none of the documents are\nrelevant to the question, or the relevant information isn't present,\nrespond with exactly: \"I don't have information about that in the\navailable documents.\" Do not attempt to answer from general knowledge,\nand do not apologize at length — state the limitation plainly and stop.\n",[30,973,974,979,984,989,994],{"__ignoreMap":28},[33,975,976],{"class":35,"line":36},[33,977,978],{"class":125},"Answer using ONLY the documents below. If none of the documents are\n",[33,980,981],{"class":35,"line":43},[33,982,983],{"class":125},"relevant to the question, or the relevant information isn't present,\n",[33,985,986],{"class":35,"line":49},[33,987,988],{"class":125},"respond with exactly: \"I don't have information about that in the\n",[33,990,991],{"class":35,"line":55},[33,992,993],{"class":125},"available documents.\" Do not attempt to answer from general knowledge,\n",[33,995,996],{"class":35,"line":61},[33,997,998],{"class":125},"and do not apologize at length — state the limitation plainly and stop.\n",[19,1000,1002],{"filename":1001,"language":22},"partial_coverage.py",[24,1003,1005],{"className":26,"code":1004,"language":22,"meta":28,"style":28},"# CRITICAL: also handle PARTIAL coverage, not just total absence.\n# Retrieved documents may be relevant but INCOMPLETE for the specific question.\n# E.g., general company PTO policy retrieved, but the employee asking is from a\n# recently-acquired subsidiary with different terms in an un-indexed document.\n\nPARTIAL_COVERAGE_INSTRUCTION = \"\"\"\nIf the documents describe a general policy but the question suggests a specific\ncircumstance (e.g., a different employment type, subsidiary, or location) that\nmight have different rules not covered here, say so explicitly rather than\nassuming the general policy applies uniformly.\n\"\"\"\n\n# \"The retrieved documents are relevant and present\" ≠\n# \"the retrieved documents fully and correctly answer THIS SPECIFIC question\"\n# A RAG prompt needs to handle partial-coverage cases explicitly, not just the\n# fully-empty-retrieval case — especially for domains where a general policy\n# stated confidently can be actively WRONG for a specific employee's circumstances.\n",[30,1006,1007,1012,1017,1022,1027,1031,1042,1047,1052,1057,1062,1067,1071,1076,1081,1086,1091],{"__ignoreMap":28},[33,1008,1009],{"class":35,"line":36},[33,1010,1011],{"class":39},"# CRITICAL: also handle PARTIAL coverage, not just total absence.\n",[33,1013,1014],{"class":35,"line":43},[33,1015,1016],{"class":39},"# Retrieved documents may be relevant but INCOMPLETE for the specific question.\n",[33,1018,1019],{"class":35,"line":49},[33,1020,1021],{"class":39},"# E.g., general company PTO policy retrieved, but the employee asking is from a\n",[33,1023,1024],{"class":35,"line":55},[33,1025,1026],{"class":39},"# recently-acquired subsidiary with different terms in an un-indexed document.\n",[33,1028,1029],{"class":35,"line":61},[33,1030,83],{"emptyLinePlaceholder":82},[33,1032,1033,1036,1039],{"class":35,"line":67},[33,1034,1035],{"class":439},"PARTIAL_COVERAGE_INSTRUCTION",[33,1037,1038],{"class":416}," =",[33,1040,1041],{"class":464}," \"\"\"\n",[33,1043,1044],{"class":35,"line":73},[33,1045,1046],{"class":464},"If the documents describe a general policy but the question suggests a specific\n",[33,1048,1049],{"class":35,"line":79},[33,1050,1051],{"class":464},"circumstance (e.g., a different employment type, subsidiary, or location) that\n",[33,1053,1054],{"class":35,"line":86},[33,1055,1056],{"class":464},"might have different rules not covered here, say so explicitly rather than\n",[33,1058,1059],{"class":35,"line":92},[33,1060,1061],{"class":464},"assuming the general policy applies uniformly.\n",[33,1063,1064],{"class":35,"line":98},[33,1065,1066],{"class":464},"\"\"\"\n",[33,1068,1069],{"class":35,"line":103},[33,1070,83],{"emptyLinePlaceholder":82},[33,1072,1073],{"class":35,"line":182},[33,1074,1075],{"class":39},"# \"The retrieved documents are relevant and present\" ≠\n",[33,1077,1078],{"class":35,"line":188},[33,1079,1080],{"class":39},"# \"the retrieved documents fully and correctly answer THIS SPECIFIC question\"\n",[33,1082,1083],{"class":35,"line":193},[33,1084,1085],{"class":39},"# A RAG prompt needs to handle partial-coverage cases explicitly, not just the\n",[33,1087,1088],{"class":35,"line":198},[33,1089,1090],{"class":39},"# fully-empty-retrieval case — especially for domains where a general policy\n",[33,1092,1093],{"class":35,"line":605},[33,1094,1095],{"class":39},"# stated confidently can be actively WRONG for a specific employee's circumstances.\n",[14,1097,1099],{"id":1098},"chunking-and-context-placement","Chunking and Context Placement",[19,1101,1103],{"filename":1102,"language":22},"chunk_placement.py",[24,1104,1106],{"className":26,"code":1105,"language":22,"meta":28,"style":28},"# Position effects (Chapter 1, Chapter 8) apply INSIDE a RAG prompt too.\n# The \"lost in the middle\" effect means a highly relevant document ranked 3rd\n# out of 6 is recalled LESS reliably than the same document ranked 1st.\n\ndef order_chunks_for_recall(retrieved_chunks: list[dict], max_chunks: int = 8) -> list[dict]:\n    \"\"\"Order chunks to exploit primacy + recency effects.\n    Most relevant FIRST and LAST; least relevant in the MIDDLE.\"\"\"\n    # Sort by relevance score (descending)\n    sorted_chunks = sorted(retrieved_chunks, key=lambda c: c[\"score\"], reverse=True)\n\n    # Interleave: best, 3rd best, 5th best, ..., 6th best, 4th best, 2nd best\n    # This puts the highest-relevance chunks at the edges (primacy + recency)\n    # and the lower-relevance chunks in the middle (where recall is weakest anyway)\n    odd = sorted_chunks[::2]  # 1st, 3rd, 5th...\n    even = sorted_chunks[1::2]  # 2nd, 4th, 6th...\n    return (odd[:max_chunks\u002F\u002F2] + even[:max_chunks\u002F\u002F2])[:max_chunks]\n\n# Also: deduplicate near-identical chunks before they reach the prompt.\n# Retrieval systems frequently surface the same policy repeated in 3 documents —\n# wastes context budget and makes citation behavior noisier without adding info.\ndef deduplicate_chunks(chunks: list[dict], similarity_threshold: float = 0.85) -> list[dict]:\n    \"\"\"Remove near-duplicate chunks before injection.\"\"\"\n    unique = []\n    for chunk in chunks:\n        if not any(jaccard_similarity(chunk[\"text\"], u[\"text\"]) > similarity_threshold\n                   for u in unique):\n            unique.append(chunk)\n    return unique\n\ndef jaccard_similarity(a: str, b: str) -> float:\n    set_a, set_b = set(a.lower().split()), set(b.lower().split())\n    intersection = set_a & set_b\n    union = set_a | set_b\n    return len(intersection) \u002F len(union) if union else 0.0\n",[30,1107,1108,1113,1118,1123,1127,1157,1162,1167,1172,1211,1215,1220,1225,1230,1249,1271,1298,1302,1307,1312,1317,1346,1351,1360,1372,1403,1416,1421,1428,1432,1457,1475,1490,1504],{"__ignoreMap":28},[33,1109,1110],{"class":35,"line":36},[33,1111,1112],{"class":39},"# Position effects (Chapter 1, Chapter 8) apply INSIDE a RAG prompt too.\n",[33,1114,1115],{"class":35,"line":43},[33,1116,1117],{"class":39},"# The \"lost in the middle\" effect means a highly relevant document ranked 3rd\n",[33,1119,1120],{"class":35,"line":49},[33,1121,1122],{"class":39},"# out of 6 is recalled LESS reliably than the same document ranked 1st.\n",[33,1124,1125],{"class":35,"line":55},[33,1126,83],{"emptyLinePlaceholder":82},[33,1128,1129,1131,1134,1137,1139,1142,1145,1147,1150,1153,1155],{"class":35,"line":61},[33,1130,429],{"class":416},[33,1132,1133],{"class":432}," order_chunks_for_recall",[33,1135,1136],{"class":125},"(retrieved_chunks: list[",[33,1138,456],{"class":439},[33,1140,1141],{"class":125},"], max_chunks: ",[33,1143,1144],{"class":439},"int",[33,1146,1038],{"class":416},[33,1148,1149],{"class":439}," 8",[33,1151,1152],{"class":125},") -> list[",[33,1154,456],{"class":439},[33,1156,459],{"class":125},[33,1158,1159],{"class":35,"line":67},[33,1160,1161],{"class":464},"    \"\"\"Order chunks to exploit primacy + recency effects.\n",[33,1163,1164],{"class":35,"line":73},[33,1165,1166],{"class":464},"    Most relevant FIRST and LAST; least relevant in the MIDDLE.\"\"\"\n",[33,1168,1169],{"class":35,"line":79},[33,1170,1171],{"class":39},"    # Sort by relevance score (descending)\n",[33,1173,1174,1177,1179,1182,1185,1188,1191,1194,1197,1200,1203,1205,1208],{"class":35,"line":86},[33,1175,1176],{"class":125},"    sorted_chunks ",[33,1178,473],{"class":416},[33,1180,1181],{"class":439}," sorted",[33,1183,1184],{"class":125},"(retrieved_chunks, ",[33,1186,1187],{"class":911},"key",[33,1189,1190],{"class":416},"=lambda",[33,1192,1193],{"class":125}," c: c[",[33,1195,1196],{"class":464},"\"score\"",[33,1198,1199],{"class":125},"], ",[33,1201,1202],{"class":911},"reverse",[33,1204,473],{"class":416},[33,1206,1207],{"class":439},"True",[33,1209,1210],{"class":125},")\n",[33,1212,1213],{"class":35,"line":92},[33,1214,83],{"emptyLinePlaceholder":82},[33,1216,1217],{"class":35,"line":98},[33,1218,1219],{"class":39},"    # Interleave: best, 3rd best, 5th best, ..., 6th best, 4th best, 2nd best\n",[33,1221,1222],{"class":35,"line":103},[33,1223,1224],{"class":39},"    # This puts the highest-relevance chunks at the edges (primacy + recency)\n",[33,1226,1227],{"class":35,"line":182},[33,1228,1229],{"class":39},"    # and the lower-relevance chunks in the middle (where recall is weakest anyway)\n",[33,1231,1232,1235,1237,1240,1243,1246],{"class":35,"line":188},[33,1233,1234],{"class":125},"    odd ",[33,1236,473],{"class":416},[33,1238,1239],{"class":125}," sorted_chunks[::",[33,1241,1242],{"class":439},"2",[33,1244,1245],{"class":125},"]  ",[33,1247,1248],{"class":39},"# 1st, 3rd, 5th...\n",[33,1250,1251,1254,1256,1259,1261,1264,1266,1268],{"class":35,"line":193},[33,1252,1253],{"class":125},"    even ",[33,1255,473],{"class":416},[33,1257,1258],{"class":125}," sorted_chunks[",[33,1260,329],{"class":439},[33,1262,1263],{"class":125},"::",[33,1265,1242],{"class":439},[33,1267,1245],{"class":125},[33,1269,1270],{"class":39},"# 2nd, 4th, 6th...\n",[33,1272,1273,1275,1278,1281,1283,1286,1288,1291,1293,1295],{"class":35,"line":198},[33,1274,855],{"class":416},[33,1276,1277],{"class":125}," (odd[:max_chunks",[33,1279,1280],{"class":416},"\u002F\u002F",[33,1282,1242],{"class":439},[33,1284,1285],{"class":125},"] ",[33,1287,520],{"class":416},[33,1289,1290],{"class":125}," even[:max_chunks",[33,1292,1280],{"class":416},[33,1294,1242],{"class":439},[33,1296,1297],{"class":125},"])[:max_chunks]\n",[33,1299,1300],{"class":35,"line":605},[33,1301,83],{"emptyLinePlaceholder":82},[33,1303,1304],{"class":35,"line":622},[33,1305,1306],{"class":39},"# Also: deduplicate near-identical chunks before they reach the prompt.\n",[33,1308,1309],{"class":35,"line":651},[33,1310,1311],{"class":39},"# Retrieval systems frequently surface the same policy repeated in 3 documents —\n",[33,1313,1314],{"class":35,"line":657},[33,1315,1316],{"class":39},"# wastes context budget and makes citation behavior noisier without adding info.\n",[33,1318,1319,1321,1324,1327,1329,1332,1335,1337,1340,1342,1344],{"class":35,"line":662},[33,1320,429],{"class":416},[33,1322,1323],{"class":432}," deduplicate_chunks",[33,1325,1326],{"class":125},"(chunks: list[",[33,1328,456],{"class":439},[33,1330,1331],{"class":125},"], similarity_threshold: ",[33,1333,1334],{"class":439},"float",[33,1336,1038],{"class":416},[33,1338,1339],{"class":439}," 0.85",[33,1341,1152],{"class":125},[33,1343,456],{"class":439},[33,1345,459],{"class":125},[33,1347,1348],{"class":35,"line":668},[33,1349,1350],{"class":464},"    \"\"\"Remove near-duplicate chunks before injection.\"\"\"\n",[33,1352,1353,1356,1358],{"class":35,"line":674},[33,1354,1355],{"class":125},"    unique ",[33,1357,473],{"class":416},[33,1359,476],{"class":125},[33,1361,1362,1364,1367,1369],{"class":35,"line":685},[33,1363,558],{"class":416},[33,1365,1366],{"class":125}," chunk ",[33,1368,564],{"class":416},[33,1370,1371],{"class":125}," chunks:\n",[33,1373,1374,1377,1380,1383,1386,1389,1392,1394,1397,1400],{"class":35,"line":717},[33,1375,1376],{"class":416},"        if",[33,1378,1379],{"class":416}," not",[33,1381,1382],{"class":439}," any",[33,1384,1385],{"class":125},"(jaccard_similarity(chunk[",[33,1387,1388],{"class":464},"\"text\"",[33,1390,1391],{"class":125},"], u[",[33,1393,1388],{"class":464},[33,1395,1396],{"class":125},"]) ",[33,1398,1399],{"class":416},">",[33,1401,1402],{"class":125}," similarity_threshold\n",[33,1404,1405,1408,1411,1413],{"class":35,"line":744},[33,1406,1407],{"class":416},"                   for",[33,1409,1410],{"class":125}," u ",[33,1412,564],{"class":416},[33,1414,1415],{"class":125}," unique):\n",[33,1417,1418],{"class":35,"line":761},[33,1419,1420],{"class":125},"            unique.append(chunk)\n",[33,1422,1423,1425],{"class":35,"line":766},[33,1424,855],{"class":416},[33,1426,1427],{"class":125}," unique\n",[33,1429,1430],{"class":35,"line":789},[33,1431,83],{"emptyLinePlaceholder":82},[33,1433,1434,1436,1439,1442,1444,1447,1449,1452,1454],{"class":35,"line":795},[33,1435,429],{"class":416},[33,1437,1438],{"class":432}," jaccard_similarity",[33,1440,1441],{"class":125},"(a: ",[33,1443,440],{"class":439},[33,1445,1446],{"class":125},", b: ",[33,1448,440],{"class":439},[33,1450,1451],{"class":125},") -> ",[33,1453,1334],{"class":439},[33,1455,1456],{"class":125},":\n",[33,1458,1459,1462,1464,1466,1469,1472],{"class":35,"line":809},[33,1460,1461],{"class":125},"    set_a, set_b ",[33,1463,473],{"class":416},[33,1465,693],{"class":439},[33,1467,1468],{"class":125},"(a.lower().split()), ",[33,1470,1471],{"class":439},"set",[33,1473,1474],{"class":125},"(b.lower().split())\n",[33,1476,1477,1480,1482,1485,1487],{"class":35,"line":818},[33,1478,1479],{"class":125},"    intersection ",[33,1481,473],{"class":416},[33,1483,1484],{"class":125}," set_a ",[33,1486,755],{"class":416},[33,1488,1489],{"class":125}," set_b\n",[33,1491,1492,1495,1497,1499,1502],{"class":35,"line":827},[33,1493,1494],{"class":125},"    union ",[33,1496,473],{"class":416},[33,1498,1484],{"class":125},[33,1500,1501],{"class":416},"|",[33,1503,1489],{"class":125},[33,1505,1506,1508,1510,1513,1516,1518,1521,1524,1527,1530],{"class":35,"line":841},[33,1507,855],{"class":416},[33,1509,771],{"class":439},[33,1511,1512],{"class":125},"(intersection) ",[33,1514,1515],{"class":416},"\u002F",[33,1517,771],{"class":439},[33,1519,1520],{"class":125},"(union) ",[33,1522,1523],{"class":416},"if",[33,1525,1526],{"class":125}," union ",[33,1528,1529],{"class":416},"else",[33,1531,1532],{"class":439}," 0.0\n",[14,1534,1536],{"id":1535},"rag-vs-fine-tuning-vs-long-context","RAG vs. Fine-Tuning vs. Long Context",[19,1538,1540],{"filename":1539,"language":22},"approach_selection.py",[24,1541,1543],{"className":26,"code":1542,"language":22,"meta":28,"style":28},"# Three different tools for three different problems — NOT interchangeable.\n\nAPPROACH_COMPARISON = {\n    \"RAG\": {\n        \"solves\": \"Grounding answers in current\u002Fspecific\u002Fprivate info at query time\",\n        \"updates\": \"Change the document store — instant, no retraining\",\n        \"provides\": \"Citations, audit trail\",\n        \"doesnt_solve\": \"Doesn't change model's underlying behavior\u002Fstyle\u002Freasoning\",\n    },\n    \"long_context\": {\n        \"solves\": \"Simple, small, static, bounded knowledge base that fits in the window\",\n        \"updates\": \"Change the document — but must re-send on every call (unless cached)\",\n        \"provides\": \"No citations by default; subject to position effects\",\n        \"doesnt_solve\": \"Doesn't scale past the context window; re-sends static content\",\n    },\n    \"fine_tuning\": {\n        \"solves\": \"Changing model's STYLE, format habits, task-specific behavior\",\n        \"updates\": \"Retraining required — slow, expensive\",\n        \"provides\": \"No citations, no audit trail for where an answer came from\",\n        \"doesnt_solve\": \"POOR tool for frequently-changing factual knowledge — updating a fact requires retraining\",\n    },\n}\n\n# RULE OF THUMB:\n# Problem: \"model doesn't know this specific\u002Fcurrent\u002Fprivate fact\"\n#   → RAG (or long-context for small static cases), NOT fine-tuning\n# Problem: \"model knows the facts but responds in wrong style\u002Fformat\u002Freasoning pattern\"\n#   → fine-tuning or better prompting (Chapters 2-7), NOT more retrieval\n",[30,1544,1545,1550,1554,1564,1572,1584,1596,1608,1620,1625,1632,1643,1654,1665,1676,1680,1687,1698,1709,1720,1731,1735,1740,1744,1749,1754,1759,1764],{"__ignoreMap":28},[33,1546,1547],{"class":35,"line":36},[33,1548,1549],{"class":39},"# Three different tools for three different problems — NOT interchangeable.\n",[33,1551,1552],{"class":35,"line":43},[33,1553,83],{"emptyLinePlaceholder":82},[33,1555,1556,1559,1561],{"class":35,"line":49},[33,1557,1558],{"class":439},"APPROACH_COMPARISON",[33,1560,1038],{"class":416},[33,1562,1563],{"class":125}," {\n",[33,1565,1566,1569],{"class":35,"line":55},[33,1567,1568],{"class":464},"    \"RAG\"",[33,1570,1571],{"class":125},": {\n",[33,1573,1574,1577,1579,1582],{"class":35,"line":61},[33,1575,1576],{"class":464},"        \"solves\"",[33,1578,631],{"class":125},[33,1580,1581],{"class":464},"\"Grounding answers in current\u002Fspecific\u002Fprivate info at query time\"",[33,1583,806],{"class":125},[33,1585,1586,1589,1591,1594],{"class":35,"line":67},[33,1587,1588],{"class":464},"        \"updates\"",[33,1590,631],{"class":125},[33,1592,1593],{"class":464},"\"Change the document store — instant, no retraining\"",[33,1595,806],{"class":125},[33,1597,1598,1601,1603,1606],{"class":35,"line":73},[33,1599,1600],{"class":464},"        \"provides\"",[33,1602,631],{"class":125},[33,1604,1605],{"class":464},"\"Citations, audit trail\"",[33,1607,806],{"class":125},[33,1609,1610,1613,1615,1618],{"class":35,"line":79},[33,1611,1612],{"class":464},"        \"doesnt_solve\"",[33,1614,631],{"class":125},[33,1616,1617],{"class":464},"\"Doesn't change model's underlying behavior\u002Fstyle\u002Freasoning\"",[33,1619,806],{"class":125},[33,1621,1622],{"class":35,"line":86},[33,1623,1624],{"class":125},"    },\n",[33,1626,1627,1630],{"class":35,"line":92},[33,1628,1629],{"class":464},"    \"long_context\"",[33,1631,1571],{"class":125},[33,1633,1634,1636,1638,1641],{"class":35,"line":98},[33,1635,1576],{"class":464},[33,1637,631],{"class":125},[33,1639,1640],{"class":464},"\"Simple, small, static, bounded knowledge base that fits in the window\"",[33,1642,806],{"class":125},[33,1644,1645,1647,1649,1652],{"class":35,"line":103},[33,1646,1588],{"class":464},[33,1648,631],{"class":125},[33,1650,1651],{"class":464},"\"Change the document — but must re-send on every call (unless cached)\"",[33,1653,806],{"class":125},[33,1655,1656,1658,1660,1663],{"class":35,"line":182},[33,1657,1600],{"class":464},[33,1659,631],{"class":125},[33,1661,1662],{"class":464},"\"No citations by default; subject to position effects\"",[33,1664,806],{"class":125},[33,1666,1667,1669,1671,1674],{"class":35,"line":188},[33,1668,1612],{"class":464},[33,1670,631],{"class":125},[33,1672,1673],{"class":464},"\"Doesn't scale past the context window; re-sends static content\"",[33,1675,806],{"class":125},[33,1677,1678],{"class":35,"line":193},[33,1679,1624],{"class":125},[33,1681,1682,1685],{"class":35,"line":198},[33,1683,1684],{"class":464},"    \"fine_tuning\"",[33,1686,1571],{"class":125},[33,1688,1689,1691,1693,1696],{"class":35,"line":605},[33,1690,1576],{"class":464},[33,1692,631],{"class":125},[33,1694,1695],{"class":464},"\"Changing model's STYLE, format habits, task-specific behavior\"",[33,1697,806],{"class":125},[33,1699,1700,1702,1704,1707],{"class":35,"line":622},[33,1701,1588],{"class":464},[33,1703,631],{"class":125},[33,1705,1706],{"class":464},"\"Retraining required — slow, expensive\"",[33,1708,806],{"class":125},[33,1710,1711,1713,1715,1718],{"class":35,"line":651},[33,1712,1600],{"class":464},[33,1714,631],{"class":125},[33,1716,1717],{"class":464},"\"No citations, no audit trail for where an answer came from\"",[33,1719,806],{"class":125},[33,1721,1722,1724,1726,1729],{"class":35,"line":657},[33,1723,1612],{"class":464},[33,1725,631],{"class":125},[33,1727,1728],{"class":464},"\"POOR tool for frequently-changing factual knowledge — updating a fact requires retraining\"",[33,1730,806],{"class":125},[33,1732,1733],{"class":35,"line":662},[33,1734,1624],{"class":125},[33,1736,1737],{"class":35,"line":668},[33,1738,1739],{"class":125},"}\n",[33,1741,1742],{"class":35,"line":674},[33,1743,83],{"emptyLinePlaceholder":82},[33,1745,1746],{"class":35,"line":685},[33,1747,1748],{"class":39},"# RULE OF THUMB:\n",[33,1750,1751],{"class":35,"line":717},[33,1752,1753],{"class":39},"# Problem: \"model doesn't know this specific\u002Fcurrent\u002Fprivate fact\"\n",[33,1755,1756],{"class":35,"line":744},[33,1757,1758],{"class":39},"#   → RAG (or long-context for small static cases), NOT fine-tuning\n",[33,1760,1761],{"class":35,"line":761},[33,1762,1763],{"class":39},"# Problem: \"model knows the facts but responds in wrong style\u002Fformat\u002Freasoning pattern\"\n",[33,1765,1766],{"class":35,"line":766},[33,1767,1768],{"class":39},"#   → fine-tuning or better prompting (Chapters 2-7), NOT more retrieval\n",[14,1770,1772],{"id":1771},"tips-tricks","💡 Tips & Tricks",[19,1774,1776],{"filename":1775,"language":22},"tips.py",[24,1777,1779],{"className":26,"code":1778,"language":22,"meta":28,"style":28},"# [Idiom] Make \"I don't know\" an explicit, first-class output. The single most\n# impactful sentence in most RAG prompts is the one telling the model exactly what\n# to say when retrieval comes up empty. Write and test it as carefully as the main\n# instruction.\n\n# [Idiom] Include source metadata in EVERY retrieved chunk (id, title, date,\n# section\u002Fpage). Costs a few extra tokens, unlocks citation, recency-preference,\n# and source-credibility instructions that are otherwise impossible to give.\n\n# [Debug] Test your RAG prompt specifically against known CONTRADICTIONS and\n# known GAPS in your document store, not just queries you know are well-covered.\n# These edge cases are where ungrounded fabrication and silent contradiction\n# resolution actually surface.\n\n# [Idiom] Separate \"no documents retrieved\" from \"documents retrieved but not\n# relevant\" if your pipeline can distinguish them. These warrant different\n# user-facing messages (knowledge base gap vs. query needs rephrasing).\n\n# [Safety] Periodically audit citations against source documents, not just\n# spot-check final answers. A systematic citation-accuracy check catches a model\n# that's begun citing plausible-but-wrong document ids — easy to miss when only\n# reading final answers for fluency.\n",[30,1780,1781,1786,1791,1796,1801,1805,1810,1815,1820,1824,1829,1834,1839,1844,1848,1853,1858,1863,1867,1872,1877,1882],{"__ignoreMap":28},[33,1782,1783],{"class":35,"line":36},[33,1784,1785],{"class":39},"# [Idiom] Make \"I don't know\" an explicit, first-class output. The single most\n",[33,1787,1788],{"class":35,"line":43},[33,1789,1790],{"class":39},"# impactful sentence in most RAG prompts is the one telling the model exactly what\n",[33,1792,1793],{"class":35,"line":49},[33,1794,1795],{"class":39},"# to say when retrieval comes up empty. Write and test it as carefully as the main\n",[33,1797,1798],{"class":35,"line":55},[33,1799,1800],{"class":39},"# instruction.\n",[33,1802,1803],{"class":35,"line":61},[33,1804,83],{"emptyLinePlaceholder":82},[33,1806,1807],{"class":35,"line":67},[33,1808,1809],{"class":39},"# [Idiom] Include source metadata in EVERY retrieved chunk (id, title, date,\n",[33,1811,1812],{"class":35,"line":73},[33,1813,1814],{"class":39},"# section\u002Fpage). Costs a few extra tokens, unlocks citation, recency-preference,\n",[33,1816,1817],{"class":35,"line":79},[33,1818,1819],{"class":39},"# and source-credibility instructions that are otherwise impossible to give.\n",[33,1821,1822],{"class":35,"line":86},[33,1823,83],{"emptyLinePlaceholder":82},[33,1825,1826],{"class":35,"line":92},[33,1827,1828],{"class":39},"# [Debug] Test your RAG prompt specifically against known CONTRADICTIONS and\n",[33,1830,1831],{"class":35,"line":98},[33,1832,1833],{"class":39},"# known GAPS in your document store, not just queries you know are well-covered.\n",[33,1835,1836],{"class":35,"line":103},[33,1837,1838],{"class":39},"# These edge cases are where ungrounded fabrication and silent contradiction\n",[33,1840,1841],{"class":35,"line":182},[33,1842,1843],{"class":39},"# resolution actually surface.\n",[33,1845,1846],{"class":35,"line":188},[33,1847,83],{"emptyLinePlaceholder":82},[33,1849,1850],{"class":35,"line":193},[33,1851,1852],{"class":39},"# [Idiom] Separate \"no documents retrieved\" from \"documents retrieved but not\n",[33,1854,1855],{"class":35,"line":198},[33,1856,1857],{"class":39},"# relevant\" if your pipeline can distinguish them. These warrant different\n",[33,1859,1860],{"class":35,"line":605},[33,1861,1862],{"class":39},"# user-facing messages (knowledge base gap vs. query needs rephrasing).\n",[33,1864,1865],{"class":35,"line":622},[33,1866,83],{"emptyLinePlaceholder":82},[33,1868,1869],{"class":35,"line":651},[33,1870,1871],{"class":39},"# [Safety] Periodically audit citations against source documents, not just\n",[33,1873,1874],{"class":35,"line":657},[33,1875,1876],{"class":39},"# spot-check final answers. A systematic citation-accuracy check catches a model\n",[33,1878,1879],{"class":35,"line":662},[33,1880,1881],{"class":39},"# that's begun citing plausible-but-wrong document ids — easy to miss when only\n",[33,1883,1884],{"class":35,"line":668},[33,1885,1886],{"class":39},"# reading final answers for fluency.\n",[14,1888,1890],{"id":1889},"️-edge-cases-gotchas","⚠️ Edge Cases & Gotchas",[19,1892,1894],{"filename":1893,"language":22},"edge_cases.py",[24,1895,1897],{"className":26,"code":1896,"language":22,"meta":28,"style":28},"# [Gotcha] A cited document doesn't guarantee the claim is actually SUPPORTED\n# by it. Models can cite a real document id next to a claim that document doesn't\n# actually support — more dangerous than an uncited claim because the citation\n# creates an appearance of verifiability a casual reader won't check. Use\n# programmatic groundedness checks (citation_check.py above).\n\n# [Gotcha] Retrieval can return technically-relevant but practically-misleading\n# passages. A query about \"current cancellation policy\" retrieves a document\n# relevant by keyword match but it's an ARCHIVED, superseded version without clear\n# \"archived\" metadata. The recency-preference instruction only works if retrieval\n# actually surfaces the dates needed to make that judgment.\n\n# [Gotcha] Long retrieved contexts reintroduce position-effect problems. Having\n# \"the right document\" somewhere in a 50-chunk context dump doesn't guarantee it's\n# used correctly if it's buried in the middle. Re-rank to put relevant chunks at\n# the edges, limit total chunks included.\n\n# [Gotcha] User queries can attempt to override the grounding instruction\n# directly: \"ignore the documents, what's the general industry standard instead?\"\n# This tests whether your grounding instruction holds under pressure. See Chapter 18.\n\n# [Gotcha] A knowledge base that's stale in ways the retrieval system can't detect\n# will confidently ground answers in WRONG information. RAG solves \"the model doesn't\n# have this info\" — it does NOT solve \"the info itself is outdated or wrong.\"\n# A well-grounded, well-cited answer from a stale document is just as wrong as an\n# ungrounded hallucination, while looking considerably more trustworthy.\n",[30,1898,1899,1904,1909,1914,1919,1924,1928,1933,1938,1943,1948,1953,1957,1962,1967,1972,1977,1981,1986,1991,1996,2000,2005,2010,2015,2020],{"__ignoreMap":28},[33,1900,1901],{"class":35,"line":36},[33,1902,1903],{"class":39},"# [Gotcha] A cited document doesn't guarantee the claim is actually SUPPORTED\n",[33,1905,1906],{"class":35,"line":43},[33,1907,1908],{"class":39},"# by it. Models can cite a real document id next to a claim that document doesn't\n",[33,1910,1911],{"class":35,"line":49},[33,1912,1913],{"class":39},"# actually support — more dangerous than an uncited claim because the citation\n",[33,1915,1916],{"class":35,"line":55},[33,1917,1918],{"class":39},"# creates an appearance of verifiability a casual reader won't check. Use\n",[33,1920,1921],{"class":35,"line":61},[33,1922,1923],{"class":39},"# programmatic groundedness checks (citation_check.py above).\n",[33,1925,1926],{"class":35,"line":67},[33,1927,83],{"emptyLinePlaceholder":82},[33,1929,1930],{"class":35,"line":73},[33,1931,1932],{"class":39},"# [Gotcha] Retrieval can return technically-relevant but practically-misleading\n",[33,1934,1935],{"class":35,"line":79},[33,1936,1937],{"class":39},"# passages. A query about \"current cancellation policy\" retrieves a document\n",[33,1939,1940],{"class":35,"line":86},[33,1941,1942],{"class":39},"# relevant by keyword match but it's an ARCHIVED, superseded version without clear\n",[33,1944,1945],{"class":35,"line":92},[33,1946,1947],{"class":39},"# \"archived\" metadata. The recency-preference instruction only works if retrieval\n",[33,1949,1950],{"class":35,"line":98},[33,1951,1952],{"class":39},"# actually surfaces the dates needed to make that judgment.\n",[33,1954,1955],{"class":35,"line":103},[33,1956,83],{"emptyLinePlaceholder":82},[33,1958,1959],{"class":35,"line":182},[33,1960,1961],{"class":39},"# [Gotcha] Long retrieved contexts reintroduce position-effect problems. Having\n",[33,1963,1964],{"class":35,"line":188},[33,1965,1966],{"class":39},"# \"the right document\" somewhere in a 50-chunk context dump doesn't guarantee it's\n",[33,1968,1969],{"class":35,"line":193},[33,1970,1971],{"class":39},"# used correctly if it's buried in the middle. Re-rank to put relevant chunks at\n",[33,1973,1974],{"class":35,"line":198},[33,1975,1976],{"class":39},"# the edges, limit total chunks included.\n",[33,1978,1979],{"class":35,"line":605},[33,1980,83],{"emptyLinePlaceholder":82},[33,1982,1983],{"class":35,"line":622},[33,1984,1985],{"class":39},"# [Gotcha] User queries can attempt to override the grounding instruction\n",[33,1987,1988],{"class":35,"line":651},[33,1989,1990],{"class":39},"# directly: \"ignore the documents, what's the general industry standard instead?\"\n",[33,1992,1993],{"class":35,"line":657},[33,1994,1995],{"class":39},"# This tests whether your grounding instruction holds under pressure. See Chapter 18.\n",[33,1997,1998],{"class":35,"line":662},[33,1999,83],{"emptyLinePlaceholder":82},[33,2001,2002],{"class":35,"line":668},[33,2003,2004],{"class":39},"# [Gotcha] A knowledge base that's stale in ways the retrieval system can't detect\n",[33,2006,2007],{"class":35,"line":674},[33,2008,2009],{"class":39},"# will confidently ground answers in WRONG information. RAG solves \"the model doesn't\n",[33,2011,2012],{"class":35,"line":685},[33,2013,2014],{"class":39},"# have this info\" — it does NOT solve \"the info itself is outdated or wrong.\"\n",[33,2016,2017],{"class":35,"line":717},[33,2018,2019],{"class":39},"# A well-grounded, well-cited answer from a stale document is just as wrong as an\n",[33,2021,2022],{"class":35,"line":744},[33,2023,2024],{"class":39},"# ungrounded hallucination, while looking considerably more trustworthy.\n",[14,2026,2028],{"id":2027},"spot-the-bug","🧠 Spot the Bug",[302,2030,2031,2032,2035],{},"A RAG HR chatbot uses: \"Answer the employee's question using the documents provided below. Documents: ",[33,2033,2034],{},"3 handbook chunks",". Question: Can I carry over unused PTO into next year?\" For a niche question about PTO for a recently-acquired subsidiary's employees (covered only in an un-indexed onboarding packet), the bot confidently answers using the general company-wide PTO policy, with no indication that this specific case might not be covered. What's missing?",[2037,2038,2039,2043,2051,2054,2060],"details",{},[2040,2041,2042],"summary",{},"Answer",[302,2044,2045,2046,2050],{},"The prompt never instructs the model on what to do when retrieved documents are RELEVANT but INCOMPLETE for the specific question — it only says \"use the documents that are there,\" which the model does, dutifully applying the general policy because nothing tells it to check whether that policy is the ",[2047,2048,2049],"em",{},"right"," one for this employee's specific situation.",[302,2052,2053],{},"This is the partial-coverage gap: the retrieved documents aren't empty or obviously irrelevant, they're just incomplete for this specific case — arguably more dangerous than a total retrieval miss because the model has real, correctly-cited material to confidently answer from. It simply isn't the material that answers this employee's actual situation.",[302,2055,2056,2057,2059],{},"The fix: add the partial-coverage instruction shown in ",[30,2058,1001],{}," — \"If the documents describe a general policy but the question suggests a specific circumstance that might have different rules not covered here, say so explicitly rather than assuming the general policy applies uniformly.\"",[302,2061,2062],{},"The lesson: \"the retrieved documents are relevant and present\" ≠ \"the retrieved documents fully and correctly answer this specific question.\" A RAG prompt needs to handle partial-coverage cases explicitly, not just the fully-empty-retrieval case.",[14,2064,2066],{"id":2065},"key-takeaways","Key Takeaways",[19,2068,2070],{"filename":2069,"language":22},"key_takeaways.py",[24,2071,2073],{"className":26,"code":2072,"language":22,"meta":28,"style":28},"\"\"\"\nRAG — grounding answers in retrieved, current, specific documents.\n\"\"\"\n\n# 1. RAG grounds answers in retrieved documents supplied at inference time,\n#    addressing the fact that pretrained knowledge is frozen, incomplete, and\n#    recalled probabilistically rather than looked up.\n\n# 2. The single highest-leverage sentence: \"use ONLY the provided documents,\n#    and say so if they don't fully answer the question.\" Without it, the model\n#    silently blends retrieved facts with pretrained knowledge — indistinguishable.\n\n# 3. Citation instructions make claims CHECKABLE — by humans and by automated\n#    groundedness checks that verify a cited claim actually appears in its source.\n\n# 4. Contradictions and gaps need explicit handling (prefer recency, surface both\n#    versions, state the limitation plainly). Also handle PARTIAL coverage: relevant\n#    but incomplete documents for a specific question — more dangerous than total miss.\n\n# 5. RAG ≠ fine-tuning ≠ long-context. RAG\u002Flong-context inject current\u002Fspecific\n#    knowledge at query time. Fine-tuning changes behavior\u002Fstyle but is POOR for\n#    frequently-changing facts. Grounding quality is capped by retrieval pipeline\n#    and document-store hygiene, not just prompt wording.\n",[30,2074,2075,2079,2084,2088,2092,2097,2102,2107,2111,2116,2121,2126,2130,2135,2140,2144,2149,2154,2159,2163,2168,2173,2178],{"__ignoreMap":28},[33,2076,2077],{"class":35,"line":36},[33,2078,1066],{"class":464},[33,2080,2081],{"class":35,"line":43},[33,2082,2083],{"class":464},"RAG — grounding answers in retrieved, current, specific documents.\n",[33,2085,2086],{"class":35,"line":49},[33,2087,1066],{"class":464},[33,2089,2090],{"class":35,"line":55},[33,2091,83],{"emptyLinePlaceholder":82},[33,2093,2094],{"class":35,"line":61},[33,2095,2096],{"class":39},"# 1. RAG grounds answers in retrieved documents supplied at inference time,\n",[33,2098,2099],{"class":35,"line":67},[33,2100,2101],{"class":39},"#    addressing the fact that pretrained knowledge is frozen, incomplete, and\n",[33,2103,2104],{"class":35,"line":73},[33,2105,2106],{"class":39},"#    recalled probabilistically rather than looked up.\n",[33,2108,2109],{"class":35,"line":79},[33,2110,83],{"emptyLinePlaceholder":82},[33,2112,2113],{"class":35,"line":86},[33,2114,2115],{"class":39},"# 2. The single highest-leverage sentence: \"use ONLY the provided documents,\n",[33,2117,2118],{"class":35,"line":92},[33,2119,2120],{"class":39},"#    and say so if they don't fully answer the question.\" Without it, the model\n",[33,2122,2123],{"class":35,"line":98},[33,2124,2125],{"class":39},"#    silently blends retrieved facts with pretrained knowledge — indistinguishable.\n",[33,2127,2128],{"class":35,"line":103},[33,2129,83],{"emptyLinePlaceholder":82},[33,2131,2132],{"class":35,"line":182},[33,2133,2134],{"class":39},"# 3. Citation instructions make claims CHECKABLE — by humans and by automated\n",[33,2136,2137],{"class":35,"line":188},[33,2138,2139],{"class":39},"#    groundedness checks that verify a cited claim actually appears in its source.\n",[33,2141,2142],{"class":35,"line":193},[33,2143,83],{"emptyLinePlaceholder":82},[33,2145,2146],{"class":35,"line":198},[33,2147,2148],{"class":39},"# 4. Contradictions and gaps need explicit handling (prefer recency, surface both\n",[33,2150,2151],{"class":35,"line":605},[33,2152,2153],{"class":39},"#    versions, state the limitation plainly). Also handle PARTIAL coverage: relevant\n",[33,2155,2156],{"class":35,"line":622},[33,2157,2158],{"class":39},"#    but incomplete documents for a specific question — more dangerous than total miss.\n",[33,2160,2161],{"class":35,"line":651},[33,2162,83],{"emptyLinePlaceholder":82},[33,2164,2165],{"class":35,"line":657},[33,2166,2167],{"class":39},"# 5. RAG ≠ fine-tuning ≠ long-context. RAG\u002Flong-context inject current\u002Fspecific\n",[33,2169,2170],{"class":35,"line":662},[33,2171,2172],{"class":39},"#    knowledge at query time. Fine-tuning changes behavior\u002Fstyle but is POOR for\n",[33,2174,2175],{"class":35,"line":668},[33,2176,2177],{"class":39},"#    frequently-changing facts. Grounding quality is capped by retrieval pipeline\n",[33,2179,2180],{"class":35,"line":674},[33,2181,2182],{"class":39},"#    and document-store hygiene, not just prompt wording.\n",[2184,2185,2186],"style",{},"html pre.shiki code .sdCPZ, html code.shiki .sdCPZ{--shiki-default:#6A737D;--shiki-github-dark:#6A737D}html .default .shiki span {color: var(--shiki-default);background: var(--shiki-default-bg);font-style: var(--shiki-default-font-style);font-weight: var(--shiki-default-font-weight);text-decoration: var(--shiki-default-text-decoration);}html .shiki span {color: var(--shiki-default);background: var(--shiki-default-bg);font-style: var(--shiki-default-font-style);font-weight: var(--shiki-default-font-weight);text-decoration: var(--shiki-default-text-decoration);}html .github-dark .shiki span {color: var(--shiki-github-dark);background: var(--shiki-github-dark-bg);font-style: var(--shiki-github-dark-font-style);font-weight: var(--shiki-github-dark-font-weight);text-decoration: var(--shiki-github-dark-text-decoration);}html.github-dark .shiki span {color: var(--shiki-github-dark);background: var(--shiki-github-dark-bg);font-style: var(--shiki-github-dark-font-style);font-weight: var(--shiki-github-dark-font-weight);text-decoration: var(--shiki-github-dark-text-decoration);}html pre.shiki code .ssxIu, html code.shiki .ssxIu{--shiki-default:#24292E;--shiki-github-dark:#E1E4E8}html pre.shiki code .sSQSC, html code.shiki .sSQSC{--shiki-default:#032F62;--shiki-default-text-decoration:underline;--shiki-github-dark:#DBEDFF;--shiki-github-dark-text-decoration:underline}html pre.shiki code .svdQ7, html code.shiki .svdQ7{--shiki-default:#D73A49;--shiki-github-dark:#F97583}html pre.shiki code .sIsaT, html code.shiki .sIsaT{--shiki-default:#6F42C1;--shiki-github-dark:#B392F0}html pre.shiki code .snvgF, html code.shiki .snvgF{--shiki-default:#005CC5;--shiki-github-dark:#79B8FF}html pre.shiki code .sJ6F3, html code.shiki .sJ6F3{--shiki-default:#032F62;--shiki-github-dark:#9ECBFF}html pre.shiki code .snRuI, html code.shiki .snRuI{--shiki-default:#22863A;--shiki-default-font-weight:bold;--shiki-github-dark:#85E89D;--shiki-github-dark-font-weight:bold}html pre.shiki code .sCrzJ, html code.shiki .sCrzJ{--shiki-default:#E36209;--shiki-github-dark:#FFAB70}",{"title":28,"searchDepth":43,"depth":43,"links":2188},[2189,2190,2191,2192,2193,2194,2195,2196,2197,2198,2199,2200],{"id":16,"depth":43,"text":17},{"id":109,"depth":43,"text":110},{"id":204,"depth":43,"text":205},{"id":307,"depth":43,"text":308},{"id":878,"depth":43,"text":879},{"id":964,"depth":43,"text":965},{"id":1098,"depth":43,"text":1099},{"id":1535,"depth":43,"text":1536},{"id":1771,"depth":43,"text":1772},{"id":1889,"depth":43,"text":1890},{"id":2027,"depth":43,"text":2028},{"id":2065,"depth":43,"text":2066},"Grounding answers in retrieved documents — the grounding instruction, citation requirements, contradiction handling, empty-retrieval fallback, chunk placement, and the RAG vs fine-tuning vs long-context decision. 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