[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"summary-a95ab1103debc0cb-fixing-rag-pipelines-by-optimizing-chunking-not-mo-summary":3,"summaries-facets-categories":86,"summary-related-a95ab1103debc0cb-fixing-rag-pipelines-by-optimizing-chunking-not-mo-summary":4103},{"id":4,"title":5,"ai":6,"body":13,"categories":51,"created_at":53,"date_modified":53,"description":45,"extension":54,"faq":53,"featured":55,"kicker_label":53,"meta":56,"navigation":67,"path":68,"published_at":69,"question":53,"scraped_at":70,"seo":71,"sitemap":72,"source_id":73,"source_name":74,"source_type":75,"source_url":76,"stem":77,"tags":78,"thumbnail_url":53,"tldr":83,"tweet":53,"unknown_tags":84,"__hash__":85},"summaries\u002Fsummaries\u002Fa95ab1103debc0cb-fixing-rag-pipelines-by-optimizing-chunking-not-mo-summary.md","Fixing RAG Pipelines by Optimizing Chunking, Not Models",{"provider":7,"model":8,"input_tokens":9,"output_tokens":10,"processing_time_ms":11,"cost_usd":12},"openrouter","google\u002Fgemini-3.1-flash-lite",4004,501,3299,0.0017525,{"type":14,"value":15,"toc":44},"minimark",[16,21,25,29,37,41],[17,18,20],"h2",{"id":19},"the-retrieval-first-debugging-approach","The Retrieval-First Debugging Approach",[22,23,24],"p",{},"When an AI assistant provides incorrect answers, the common instinct is to blame the LLM or attempt to \"fix\" it with prompt engineering. However, 73% of RAG failures are actually rooted in poor data retrieval. Instead of upgrading models, developers should inspect the raw chunks returned by their vector database. In many cases, the LLM is not hallucinating; it is simply being fed incomplete or irrelevant context that makes it impossible to answer the user's query correctly.",[17,26,28],{"id":27},"the-failure-of-naive-chunking","The Failure of Naive Chunking",[22,30,31,32,36],{},"Tools like ",[33,34,35],"code",{},"pgvector"," perform similarity searches based on vector distance, but they lack semantic awareness. A naive chunking strategy—such as splitting text into fixed 512-token blobs—often results in fragments that start or end mid-sentence. These fragments frequently strip away the necessary context required to answer specific questions. If the top-ranked chunk contains irrelevant information (e.g., a cancellation policy instead of a renewal window), the LLM will inevitably produce a \"hallucination\" based on the garbage data it was provided.",[17,38,40],{"id":39},"practical-steps-for-improvement","Practical Steps for Improvement",[22,42,43],{},"To resolve these issues, developers must move beyond treating the RAG pipeline as a black box. The debugging workflow should start by logging and manually reviewing the exact chunks retrieved by the vector search before they reach the LLM. By auditing these chunks, you can identify patterns where the retrieval logic fails to capture complete thoughts or relevant sections. Improving retrieval quality—often through better chunking logic, metadata filtering, or hybrid search—is significantly more impactful than model swapping when dealing with domain-specific knowledge bases.",{"title":45,"searchDepth":46,"depth":46,"links":47},"",2,[48,49,50],{"id":19,"depth":46,"text":20},{"id":27,"depth":46,"text":28},{"id":39,"depth":46,"text":40},[52],"AI & LLMs",null,"md",false,{"content_references":57,"triage":62},[58],{"type":59,"title":35,"url":60,"context":61},"tool","https:\u002F\u002Fgithub.com\u002Fpgvector\u002Fpgvector","mentioned",{"relevance":63,"novelty":64,"quality":64,"actionability":64,"composite":65,"reasoning":66},5,4,4.35,"Category: AI & LLMs. The article addresses a critical aspect of RAG pipelines, focusing on chunking strategies to improve data retrieval, which is a key pain point for developers integrating AI. It provides practical steps for improvement, such as auditing retrieved chunks, which can be directly applied to enhance AI-powered product features.",true,"\u002Fsummaries\u002Fa95ab1103debc0cb-fixing-rag-pipelines-by-optimizing-chunking-not-mo-summary","2026-05-20 17:43:58","2026-05-21 23:00:39",{"title":5,"description":45},{"loc":68},"a95ab1103debc0cb","Level Up Coding","article","https:\u002F\u002Flevelup.gitconnected.com\u002Fyour-rails-rag-pipeline-is-not-hallucinating-it-is-retrieving-garbage-94e764843d10?source=rss----5517fd7b58a6---4","summaries\u002Fa95ab1103debc0cb-fixing-rag-pipelines-by-optimizing-chunking-not-mo-summary",[79,80,81,82],"llm","data-science","rag","postgresql","Most RAG failures are caused by poor data retrieval, not model hallucinations. Improving chunking strategy and inspecting raw retrieved data is the most effective way to improve accuracy.",[81,82],"8DLbHZt_Ihe8XBYyaBcniHSgTUubKmXMYFyNbAEyI4Y",[87,90,93,95,98,101,103,105,107,109,111,113,116,118,120,122,124,126,128,130,132,134,136,138,140,142,145,148,150,152,155,157,159,162,164,166,168,170,172,174,176,178,180,182,184,187,189,191,193,195,197,199,201,203,205,207,209,211,213,215,217,219,221,223,225,227,229,231,233,235,237,239,241,243,245,247,249,251,253,255,257,259,261,263,265,267,269,271,273,275,277,279,281,283,285,287,289,291,293,295,297,299,301,303,305,307,309,311,313,315,317,319,321,323,325,327,329,331,333,335,337,339,341,343,345,347,349,351,353,355,357,359,361,363,365,367,369,371,373,375,377,379,381,383,385,387,389,391,393,395,397,399,401,403,405,407,409,411,413,415,417,419,421,423,425,427,429,431,433,435,437,439,441,443,445,447,449,451,453,455,457,459,461,463,465,467,469,471,473,475,477,479,481,483,485,487,489,491,493,495,497,499,501,503,505,507,509,511,513,515,517,519,521,523,525,527,529,531,533,535,537,539,541,543,545,547,549,551,553,555,557,559,561,563,565,567,569,571,573,575,577,579,581,583,585,587,589,591,593,595,597,599,601,603,605,607,609,611,613,615,617,619,621,623,625,627,629,631,633,635,637,639,641,643,645,647,649,651,653,655,657,659,661,663,665,667,669,671,673,675,677,679,681,683,685,687,689,691,693,695,697,699,701,703,705,707,709,711,713,715,717,719,721,723,725,727,729,731,733,735,737,739,741,743,745,747,749,751,753,755,757,759,761,763,765,767,769,771,773,775,777,779,781,783,785,787,789,791,793,795,797,799,801,803,805,807,809,811,813,815,817,819,821,823,825,827,829,831,833,835,837,839,841,843,845,847,849,851,853,855,857,859,861,863,865,867,869,871,873,875,877,879,881,883,885,887,889,891,893,895,897,899,901,903,905,907,909,911,913,915,917,919,921,923,925,927,929,931,933,935,937,939,941,943,945,947,949,951,953,955,957,959,961,963,965,967,969,971,973,975,977,979,981,983,985,987,989,991,993,995,997,999,1001,1003,1005,1007,1009,1011,1013,1015,1017,1019,1021,1023,1025,1027,1029,1031,1033,1035,1037,1039,1041,1043,1045,1047,1049,1051,1053,1055,1057,1059,1061,1063,1065,1067,1069,1071,1073,1075,1077,1079,1081,1083,1085,1087,1089,1091,1093,1095,1097,1099,1101,1103,1105,1107,1109,1111,1113,1115,1117,1119,1121,1123,1125,1127,1129,1131,1133,1135,1137,1139,1141,1143,1145,1147,1149,1151,1153,1155,1157,1159,1161,1163,1165,1167,1169,1171,1173,1175,1177,1179,1181,1183,1185,1187,1189,1191,1193,1195,1197,1199,1201,1203,1205,1207,1209,1211,1213,1215,1217,1219,1221,1223,1225,1227,1229,1231,1233,1235,1237,1239,1241,1243,1245,1247,1249,1251,1253,1255,1257,1259,1261,1263,1265,1267,1269,1271,1273,1275,1277,1279,1281,1283,1285,1287,1289,1291,1293,1295,1297,1299,1301,1303,1305,1307,1309,1311,1313,1315,1317,1319,1321,1323,1325,1327,1329,1331,1333,1335,1337,1339,1341,1343,1345,1347,1349,1351,1353,1355,1357,1359,1361,1363,1365,1367,1369,1371,1373,1375,1377,1379,1381,1383,1385,1387,1389,1391,1393,1395,1397,1399,1401,1403,1405,1407,1409,1411,1413,1415,1417,1419,1421,1423,1425,1427,1429,1431,1433,1435,1437,1439,1441,1443,1445,1447,1449,1451,1453,1455,1457,1459,1461,1463,1465,1467,1469,1471,1473,1475,1477,1479,1481,1483,1485,1487,1489,1491,1493,1495,1497,1499,1501,1503,1505,1507,1509,1511,1513,1515,1517,1519,1521,1523,1525,1527,1529,1531,1533,1535,1537,1539,1541,1543,1545,1547,1549,1551,1553,1555,1557,1559,1561,1563,1565,1567,1569,1571,1573,1575,1577,1579,1581,1583,1585,1587,1589,1591,1593,1595,1597,1599,1601,1603,1605,1607,1609,1611,1613,1615,1617,1619,1621,1623,1625,1627,1629,1631,1633,1635,1637,1639,1641,1643,1645,1647,1649,1651,1653,1655,1657,1659,1661,1663,1665,1667,1669,1671,1673,1675,1677,1679,1681,1683,1685,1687,1689,1691,1693,1695,1697,1699,1701,1703,1705,1707,1709,1711,1713,1715,1717,1719,1721,1723,1725,1727,1729,1731,1733,1735,1737,1739,1741,1743,1745,1747,1749,1751,1753,1755,1757,1759,1761,1763,1765,1767,1769,1771,1773,1775,1777,1779,1781,1783,1785,1787,1789,1791,1793,1795,1797,1799,1801,1803,1805,1807,1809,1811,1813,1815,1817,1819,1821,1823,1825,1827,1829,1831,1833,1835,1837,1839,1841,1843,1845,1847,1849,1851,1853,1855,1857,1859,1861,1863,1865,1867,1869,1871,1873,1875,1877,1879,1881,1883,1885,1887,1889,1891,1893,1895,1897,1899,1901,1903,1905,1907,1909,1911,1913,1915,1917,1919,1921,1923,1925,1927,1929,1931,1933,1935,1937,1939,1941,1943,1945,1947,1949,1951,1953,1955,1957,1959,1961,1963,1965,1967,1969,1971,1973,1975,1977,1979,1981,1983,1985,1987,1989,1991,1993,1995,1997,1999,2001,2003,2005,2007,2009,2011,2013,2015,2017,2019,2021,2023,2025,2027,2029,2031,2033,2035,2037,2039,2041,2043,2045,2047,2049,2051,2053,2055,2057,2059,2061,2063,2065,2067,2069,2071,2073,2075,2077,2079,2081,2083,2085,2087,2089,2091,2093,2095,2097,2099,2101,2103,2105,2107,2109,2111,2113,2115,2117,2119,2121,2123,2125,2127,2129,2131,2133,2135,2137,2139,2141,2143,2145,2147,2149,2151,2153,2155,2157,2159,2161,2163,2165,2167,2169,2171,2173,2175,2177,2179,2181,2183,2185,2187,2189,2191,2193,2195,2197,2199,2201,2203,2205,2207,2209,2211,2213,2215,2217,2219,2221,2223,2225,2227,2229,2231,2233,2235,2237,2239,2241,2243,2245,2247,2249,2251,2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No errors trigger, no logs flag issues—failures surface only via delayed user complaints weeks later. Tweaking parameters like embedding models or top-k doesn't fix this; it's baked into relying solely on vector similarity, which ignores entity relationships and document structure.",[17,4123,4125],{"id":4124},"graphrag-leverage-knowledge-graphs-for-relational-context","GraphRAG: Leverage Knowledge Graphs for Relational Context",[22,4127,4128],{},"GraphRAG overlays a knowledge graph on your data to explicitly map relationships between entities (e.g., people, places, concepts). Retrieval pulls connected subgraphs, not just isolated chunks, enabling the LLM to reason over interconnected facts. Use this when your domain has complex entities—like legal docs or enterprise knowledge bases—where proximity alone fails but relational paths reveal truth. Trade-off: Higher upfront indexing cost for graph construction, but gains precision on global queries.",[17,4130,4132],{"id":4131},"vectorless-rag-llm-driven-reasoning-over-raw-structure","Vectorless RAG: LLM-Driven Reasoning Over Raw Structure",[22,4134,4135],{},"Vectorless RAG ditches vector databases entirely, feeding the LLM hierarchical document outlines or tree structures (e.g., via markdown headers, XML tags). The model navigates and summarizes sections dynamically without embeddings. Ideal for hierarchical content like reports or codebases, where structure guides relevance better than semantics. Trade-off: Slower at scale without vector speedups, but avoids embedding drift and shines on precise, local queries.",[22,4137,4138],{},"Neither replaces vector RAG as a drop-in—pick GraphRAG for entity-heavy data, Vectorless for structured docs. Both eliminate the 'smoothly wrong' failure by addressing what vectors miss: relationships or hierarchy.",{"title":45,"searchDepth":46,"depth":46,"links":4140},[4141,4142,4143],{"id":4117,"depth":46,"text":4118},{"id":4124,"depth":46,"text":4125},{"id":4131,"depth":46,"text":4132},[],{"content_references":4146,"triage":4147},[],{"relevance":63,"novelty":64,"quality":64,"actionability":64,"composite":65,"reasoning":4148},"Category: AI & LLMs. The article provides a deep dive into advanced context engineering techniques for AI models, specifically addressing the limitations of traditional vector RAG and presenting innovative alternatives like GraphRAG and Vectorless RAG. It offers actionable insights on when to use each method based on specific data types, making it highly relevant for product builders looking to implement AI effectively.","\u002Fsummaries\u002Fb395b6790f7cac0d-graphrag-and-vectorless-rag-fix-vector-rag-s-silen-summary","2026-05-03 07:34:05","2026-05-03 17:00:59",{"title":4106,"description":45},{"loc":4149},"b395b6790f7cac0d","Towards AI","https:\u002F\u002Fpub.towardsai.net\u002Fgraphrag-vs-vectorless-rag-vs-vector-rag-a-2026-guide-to-advanced-context-engineering-e8e9264cab38?source=rss----98111c9905da---4","summaries\u002Fb395b6790f7cac0d-graphrag-and-vectorless-rag-fix-vector-rag-s-silen-summary",[79,81],"Vector RAG structurally fails by confidently hallucinating on semantically similar but incorrect chunks with no errors logged. GraphRAG maps entity relationships via graphs; Vectorless RAG skips vectors for LLM reasoning over document structure—each excels where the other can't.",[81],"SRq2FDVakmAyBlM0jCkkgfsMc3ogQZXFSpDxvTIhVKA",{"id":4163,"title":4164,"ai":4165,"body":4170,"categories":4207,"created_at":53,"date_modified":53,"description":45,"extension":54,"faq":53,"featured":55,"kicker_label":53,"meta":4208,"navigation":67,"path":4209,"published_at":4210,"question":53,"scraped_at":53,"seo":4211,"sitemap":4212,"source_id":4213,"source_name":4155,"source_type":75,"source_url":4214,"stem":4215,"tags":4216,"thumbnail_url":53,"tldr":4217,"tweet":53,"unknown_tags":4218,"__hash__":4219},"summaries\u002Fsummaries\u002Fvector-rag-s-semantic-trap-wrong-chunks-confident--summary.md","Vector RAG's Semantic Trap: Wrong Chunks, Confident Errors",{"provider":7,"model":4108,"input_tokens":4166,"output_tokens":4167,"processing_time_ms":4168,"cost_usd":4169},3649,1333,14692,0.0009259,{"type":14,"value":4171,"toc":4203},[4172,4176,4179,4182,4186,4194,4197],[17,4173,4175],{"id":4174},"production-pitfalls-of-vector-rag","Production Pitfalls of Vector RAG",[22,4177,4178],{},"Vector-based RAG systems fail by retrieving chunks semantically close to queries but irrelevant to the actual answer, like pulling the wrong clause from a 200-page contract. The LLM then faithfully synthesizes this misleading input into a convincingly wrong response with high confidence scores. This issue hides in demos but dominates production, where retriever errors compound without the model noticing mismatches.",[22,4180,4181],{},"By 2026, this splits teams into vector-based and vectorless RAG camps, as semantic similarity alone proves unreliable for precise retrieval.",[17,4183,4185],{"id":4184},"vector-rag-mechanics-exposed","Vector RAG Mechanics Exposed",[22,4187,4188,4189,4193],{},"Vector RAG operates in three steps: (1) Split documents into 256–1024 token chunks; (2) Convert chunks to vectors via embedding models; (3) ",[4190,4191,4192],"span",{},"Truncated in source",".",[22,4195,4196],{},"This vector approach prioritizes semantic proximity over exact matches, enabling the core failure: relevant-looking but incorrect retrievals that evade hallucination checks.",[22,4198,4199],{},[4200,4201,4202],"em",{},"Note: Source content truncates mid-explanation of vector conversion, omitting the 4 promised vectorless approaches.",{"title":45,"searchDepth":46,"depth":46,"links":4204},[4205,4206],{"id":4174,"depth":46,"text":4175},{"id":4184,"depth":46,"text":4185},[],{},"\u002Fsummaries\u002Fvector-rag-s-semantic-trap-wrong-chunks-confident-summary","2026-04-08 21:21:21",{"title":4164,"description":45},{"loc":4209},"72457d250b5e57fb","https:\u002F\u002Funknown","summaries\u002Fvector-rag-s-semantic-trap-wrong-chunks-confident--summary",[79,81],"Vector RAG retrieves semantically similar but irrelevant text chunks, yielding high-confidence wrong answers that fail in production—not demos—driving 2026 shift to vectorless approaches.",[81],"mYh0b2jvuF2Lo8BpX8gIJCYCssZBj4HednRM6zRzW4w",{"id":4221,"title":4222,"ai":4223,"body":4228,"categories":4265,"created_at":53,"date_modified":53,"description":45,"extension":54,"faq":53,"featured":55,"kicker_label":53,"meta":4266,"navigation":67,"path":4308,"published_at":53,"question":53,"scraped_at":4309,"seo":4310,"sitemap":4311,"source_id":4312,"source_name":4313,"source_type":75,"source_url":4314,"stem":4315,"tags":4316,"thumbnail_url":53,"tldr":4318,"tweet":53,"unknown_tags":4319,"__hash__":4320},"summaries\u002Fsummaries\u002Fb001c7b9229645e8-80-ai-failures-stem-from-missing-ai-ready-data-summary.md","80% AI Failures Stem from Missing AI-Ready Data",{"provider":7,"model":4108,"input_tokens":4224,"output_tokens":4225,"processing_time_ms":4226,"cost_usd":4227},7982,3021,25939,0.00308425,{"type":14,"value":4229,"toc":4260},[4230,4234,4237,4241,4244,4247,4250,4254,4257],[17,4231,4233],{"id":4232},"ai-projects-fail-at-scale-without-ai-ready-data","AI Projects Fail at Scale Without AI-Ready Data",[22,4235,4236],{},"AI initiatives surge—72% of organizations use AI in at least one function (McKinsey 2024), spending hit $13.8B in 2024 (six-fold from 2023)—yet over 80% fail, twice IT project rates. Only 48% reach production (8 months from prototype), and 30% of GenAI projects abandon post-POC by 2025 due to poor data quality, risks, costs, or unclear value (Gartner). Workers save 1 hour\u002Fday on tasks (Adecco study of 35K across 27 economies), but unreliable outcomes halt scaling. Root cause: not data scarcity (39% Gartner barrier), but absence of AI-ready data. Traditional management suits analytics but ignores AI's iterative, contextual needs—43% cite data quality\u002Freadiness as top obstacle (Informatica CDO Insights 2025).",[17,4238,4240],{"id":4239},"three-distinctions-of-ai-ready-data-management","Three Distinctions of AI-Ready Data Management",[22,4242,4243],{},"AI-ready data demands dynamic practices beyond 'fit-and-forget' pipelines. Answer 5 questions for context: What use cases? Maturity level? Skills? No universal formula—it's iterative per enterprise, enabled via metadata for discovery\u002Flineage.",[22,4245,4246],{},"Quality exceeds traditional accuracy: data must be fit-for-purpose (structured\u002Funstructured per GenAI\u002FLLM needs), representative (include outliers for training, tracked via provenance), open-ended (iterative changes post-outcomes), and compliant (evolving privacy regs). Metadata + governance ensure traceability, avoiding biases or sanctions (e.g., misdiagnosis).",[22,4248,4249],{},"Path is evolutionary: 75% prioritize AI-ready data investments next 2-3 years (Gartner). Shift from model-building to foundations handling RAG, feature selection, prompts—data prep dominates effort. Avoid hype pitfalls like unpredictable outputs from legacy data.",[17,4251,4253],{"id":4252},"elements-of-reliable-foundations-and-acceleration","Elements of Reliable Foundations and Acceleration",[22,4255,4256],{},"Core: relevant (contextual via metadata), responsible (governed\u002Funbiased), reliable (complete\u002Fresilient at scale). Use AI-powered platforms to automate—e.g., GenAI interfaces cut months to instant access, enabling non-technical tasks.",[22,4258,4259],{},"Examples: Paycor, Citizens, Holiday Inn use such systems for secure, democratized data, boosting AI decisions. Build on universal metadata for multi-cloud flexibility, no lock-in. Result: grounded GenAI apps that deploy fast, comply, and scale without 'hilarious-to-dangerous' errors.",{"title":45,"searchDepth":46,"depth":46,"links":4261},[4262,4263,4264],{"id":4232,"depth":46,"text":4233},{"id":4239,"depth":46,"text":4240},{"id":4252,"depth":46,"text":4253},[52],{"content_references":4267,"triage":4306},[4268,4273,4276,4279,4282,4285,4289,4292,4294,4299,4303],{"type":4269,"title":4270,"url":4271,"context":4272},"report","McKinsey Global Survey on AI (2024)","https:\u002F\u002Fwww.mckinsey.com\u002Fcapabilities\u002Fquantumblack\u002Four-insights\u002Fthe-state-of-ai","cited",{"type":4269,"title":4274,"url":4275,"context":4272},"KPMG GenAI Survey August 2024","https:\u002F\u002Fkpmg.com\u002Fkpmg-us\u002Fcontent\u002Fdam\u002Fkpmg\u002Fcorporate-communications\u002Fpdf\u002F2024\u002Fkpmg-genai-survey-august-2024.pdf",{"type":4269,"title":4277,"url":4278,"context":4272},"Informatica CDO Insights 2025","https:\u002F\u002Fwww.informatica.com\u002Flp\u002Fcdo-insights-2025_5039.html",{"type":4269,"title":4280,"url":4281,"context":4272},"Adecco Group AI Productivity Study","https:\u002F\u002Fwww.adeccogroup.com\u002Four-group\u002Fmedia\u002Fpress-releases\u002Fai-saves-workers-an-average-of-one-hour-each-day",{"type":4269,"title":4283,"url":4284,"context":4272},"RAND Research Report RRA2680-1","https:\u002F\u002Fwww.rand.org\u002Fpubs\u002Fresearch_reports\u002FRRA2680-1.html",{"type":4286,"title":4287,"url":4288,"context":4272},"other","Gartner Survey Finds Generative AI Is Now the Most Frequently Deployed AI Solution","https:\u002F\u002Fwww.gartner.com\u002Fen\u002Fnewsroom\u002Fpress-releases\u002F2024-05-07-gartner-survey-finds-generative-ai-is-now-the-most-frequently-deployed-ai-solution-in-organizations",{"type":4286,"title":4290,"url":4291,"context":4272},"Gartner Predicts 30% of Generative AI Projects Will be Abandoned After Proof of Concept by End of 2025","https:\u002F\u002Fwww.gartner.com\u002Fen\u002Fnewsroom\u002Fpress-releases\u002F2024-07-29-gartner-predicts-30-percent-of-generative-ai-projects-will-be-abandoned-after-proof-of-concept-by-end-of-2025",{"type":4269,"title":4293,"context":4272},"Gartner’s 2024 Evolution of Data Management as a Dedicated Function Survey",{"type":4295,"title":4296,"author":4297,"publisher":4298,"context":4272},"paper","A Journey Guide to Delivering AI Success Through ’AI-Ready’ Data","Ehtisham Zaidi, Roxane Edjlali","Gartner",{"type":59,"title":4300,"url":4301,"context":4302},"Informatica Intelligent Data Management Cloud (IDMC)","https:\u002F\u002Fwww.informatica.com\u002Fplatform.html","recommended",{"type":59,"title":4304,"url":4305,"context":4302},"CLAIRE® Copilot","https:\u002F\u002Fwww.informatica.com\u002Fabout-us\u002Fclaire.html",{"relevance":63,"novelty":64,"quality":64,"actionability":64,"composite":65,"reasoning":4307},"Category: Data Science & Visualization. The article addresses a critical pain point for AI builders regarding the importance of AI-ready data, providing actionable insights on how to manage data effectively for AI projects. It emphasizes the need for dynamic data practices and governance, which are essential for scaling AI initiatives.","\u002Fsummaries\u002Fb001c7b9229645e8-80-ai-failures-stem-from-missing-ai-ready-data-summary","2026-04-15 15:28:12",{"title":4222,"description":45},{"loc":4308},"b001c7b9229645e8","__oneoff__","https:\u002F\u002Fwww.informatica.com\u002Fblogs\u002Fthe-surprising-reason-most-ai-projects-fail-and-how-to-avoid-it-at-your-enterprise.html","summaries\u002Fb001c7b9229645e8-80-ai-failures-stem-from-missing-ai-ready-data-summary",[79,80,4317],"saas","Over 80% of AI projects fail due to lack of AI-ready data, not raw data volume. Build dynamic, contextual foundations with metadata intelligence, governance, and use-case specificity to scale reliably—traditional data practices fall short.",[],"kTuds-_7RpJA9oLTsLt0OaxJiRVhuHznK9Q2wUNPlZk",{"id":4322,"title":4323,"ai":4324,"body":4329,"categories":4413,"created_at":53,"date_modified":53,"description":45,"extension":54,"faq":53,"featured":55,"kicker_label":53,"meta":4414,"navigation":67,"path":4423,"published_at":4424,"question":53,"scraped_at":4425,"seo":4426,"sitemap":4427,"source_id":4428,"source_name":4429,"source_type":75,"source_url":4430,"stem":4431,"tags":4432,"thumbnail_url":53,"tldr":4434,"tweet":53,"unknown_tags":4435,"__hash__":4436},"summaries\u002Fsummaries\u002Fa4d878246a3eadf0-context-engineering-unlocks-ai-via-rag-graphrag-summary.md","Context Engineering Unlocks AI via RAG & GraphRAG",{"provider":7,"model":4108,"input_tokens":4325,"output_tokens":4326,"processing_time_ms":4327,"cost_usd":4328},5355,1544,13322,0.00182035,{"type":14,"value":4330,"toc":4408},[4331,4335,4338,4342,4345,4374,4377,4381,4384,4405],[17,4332,4334],{"id":4333},"context-trumps-model-reasoning-for-reliable-ai","Context Trumps Model Reasoning for Reliable AI",[22,4336,4337],{},"Frontier AI models excel at reasoning but fail on relevance without proper context, leading to confidently wrong outputs. Context engineering delivers the right data—discovered, understood, and applied in real-time—while respecting governance. For example, preparing for a client meeting, a context-aware system pulls recent support tickets and deal history (e.g., upcoming renewal) but excludes internal pricing due to role-based access, producing a useful prep document instead of a generic template. This shifts the bottleneck from model limits to infrastructure: data spans databases, APIs, SaaS, cloud\u002Fon-prem, structured\u002Funstructured sources, with varying freshness and permissions.",[17,4339,4341],{"id":4340},"four-pillars-build-contextual-intelligence","Four Pillars Build Contextual Intelligence",[22,4343,4344],{},"Effective context engineering rests on four interconnected elements:",[4346,4347,4348,4356,4362,4368],"ol",{},[4349,4350,4351,4355],"li",{},[4352,4353,4354],"strong",{},"Connected Access",": Use zero-copy federation to query data in place across the estate, ensuring freshness and intact access controls without centralizing copies.",[4349,4357,4358,4361],{},[4352,4359,4360],{},"Knowledge Layer",": Add meaning to raw data via entity resolution, relationship mapping (hierarchies), decision traces, and institutional knowledge.",[4349,4363,4364,4367],{},[4352,4365,4366],{},"Precision Retrieval",": Deliver only relevant context filtered by intent, role, time, and policy—avoiding 'more is better' by excluding noise.",[4349,4369,4370,4373],{},[4352,4371,4372],{},"Runtime Governance",": Enforce permissions live at retrieval (e.g., can this agent query this source?) and response (e.g., include this result?).",[22,4375,4376],{},"These pillars provide visibility (access), meaning (knowledge), relevance (retrieval), and defensibility (governance), enabling better decisions in agentic AI.",[17,4378,4380],{"id":4379},"advanced-rag-evolves-precision-retrieval","Advanced RAG Evolves Precision Retrieval",[22,4382,4383],{},"Basic RAG chunks documents, embeds vectors, and retrieves by similarity—great for simple lookups but limited for complex needs. Upgrade with:",[4385,4386,4387,4393,4399],"ul",{},[4349,4388,4389,4392],{},[4352,4390,4391],{},"Agentic RAG",": Agents iteratively query, assess results, and fetch more if needed.",[4349,4394,4395,4398],{},[4352,4396,4397],{},"GraphRAG",": Navigates via graph structures—finds entities connected to a query (e.g., client-related docs via relationships) for structured precision, with vectors filling details.",[4349,4400,4401,4404],{},[4352,4402,4403],{},"Context Compression",": Summarizes long docs, ranks by task relevance, and prioritizes signal over noise, respecting context window limits even in large-window models.",[22,4406,4407],{},"Combined, these make context lean, iterative, and relational, maximizing model performance: agentic decides what to retrieve, GraphRAG structures it, compression refines it.",{"title":45,"searchDepth":46,"depth":46,"links":4409},[4410,4411,4412],{"id":4333,"depth":46,"text":4334},{"id":4340,"depth":46,"text":4341},{"id":4379,"depth":46,"text":4380},[],{"content_references":4415,"triage":4421},[4416,4418],{"type":4286,"title":4397,"url":4417,"context":4302},"https:\u002F\u002Fibm.biz\u002FBdpyvE",{"type":4286,"title":4419,"url":4420,"context":4302},"IBM AI Newsletter","https:\u002F\u002Fibm.biz\u002FBdpyvX",{"relevance":63,"novelty":64,"quality":64,"actionability":64,"composite":65,"reasoning":4422},"Category: AI & LLMs. The article provides a deep dive into context engineering, which is crucial for building AI systems that rely on relevant data retrieval, addressing a core pain point for developers integrating AI features. It outlines specific frameworks like Agentic RAG and GraphRAG, offering actionable insights on improving AI output relevance.","\u002Fsummaries\u002Fa4d878246a3eadf0-context-engineering-unlocks-ai-via-rag-graphrag-summary","2026-05-02 11:01:22","2026-05-03 16:43:37",{"title":4323,"description":45},{"loc":4423},"0e40dfef1a234e9b","IBM Technology","https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=pN-LfxNFiTc","summaries\u002Fa4d878246a3eadf0-context-engineering-unlocks-ai-via-rag-graphrag-summary",[79,4433,81],"agents","Context—not model intelligence—is AI's main bottleneck. Build contextual systems with connected access, knowledge layers, precision retrieval (agentic RAG, GraphRAG, compression), and runtime governance for relevant, governed outputs.",[81],"OPZuhkr26xZ9TLjwQYk3IHYgu5XQeEYNFK_ybrnVqQQ"]