[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"summary-86e4ca3c0a4555e4-claude-opus-4-7-prompt-tweaks-boost-safety-and-too-summary":3,"summaries-facets-categories":109,"summary-related-86e4ca3c0a4555e4-claude-opus-4-7-prompt-tweaks-boost-safety-and-too-summary":3679},{"id":4,"title":5,"ai":6,"body":13,"categories":58,"created_at":59,"date_modified":59,"description":51,"extension":60,"faq":59,"featured":61,"kicker_label":59,"meta":62,"navigation":91,"path":92,"published_at":59,"question":59,"scraped_at":93,"seo":94,"sitemap":95,"source_id":96,"source_name":97,"source_type":98,"source_url":99,"stem":100,"tags":101,"thumbnail_url":59,"tldr":106,"tweet":59,"unknown_tags":107,"__hash__":108},"summaries\u002Fsummaries\u002F86e4ca3c0a4555e4-claude-opus-4-7-prompt-tweaks-boost-safety-and-too-summary.md","Claude Opus 4.7 Prompt Tweaks Boost Safety and Tool Use",{"provider":7,"model":8,"input_tokens":9,"output_tokens":10,"processing_time_ms":11,"cost_usd":12},"openrouter","x-ai\u002Fgrok-4.1-fast",5724,1880,12240,0.00157795,{"type":14,"value":15,"toc":50},"minimark",[16,21,29,33,36,40,43,47],[17,18,20],"h2",{"id":19},"safety-and-ethical-guardrails-tightened","Safety and Ethical Guardrails Tightened",[22,23,24,25],"p",{},"Child safety now triggers persistent caution: once Claude refuses a request, it approaches all subsequent conversation turns with extreme caution, wrapped in a dedicated ",[26,27,28],"child-safety",{}," tag. A new disordered eating section prohibits precise nutrition, diet, or exercise guidance—including numbers, targets, or plans—even if aimed at harm reduction, to avoid triggering tendencies. Screenshot attacks prompting yes\u002Fno on controversies are countered by allowing nuanced responses with explanations why short answers fail complex issues. Political facts updated implicitly via January 2026 knowledge cutoff, dropping explicit \"Donald Trump is president since Jan 20, 2025\" clarification from 4.6.",[17,30,32],{"id":31},"task-execution-favors-tools-over-queries","Task Execution Favors Tools Over Queries",[22,34,35],{},"Ambiguous requests get proactive resolution: make reasonable assumptions instead of interviewing users, unless unanswerable (e.g., missing attachment). Prefer tool calls—like searching, location lookup, or calendar checks—to fill gaps before asking users. New tool_search integration mandates checking for deferred tools before claiming lacks access to data like location or files. Once started, complete tasks fully rather than halting midway. Less pushy: respect user signals to end conversations without eliciting more turns.",[17,37,39],{"id":38},"conciseness-and-style-polish","Conciseness and Style Polish",[22,41,42],{},"Responses stay focused and brief to avoid overwhelming users, disclosing caveats succinctly while prioritizing the main answer. Removed 4.6 rules against emotes in asterisks, \"genuinely\u002Fhonestly\u002Fstraightforward\" since the model no longer needs them. Developer platform renamed to Claude Platform; tools list adds Claude in PowerPoint (slides agent) alongside Chrome browsing and Excel agents.",[17,44,46],{"id":45},"tools-unchanged-but-fully-listed","Tools Unchanged but Fully Listed",[22,48,49],{},"Asking Claude directly reveals 23 tools including ask_user_input_v0, bash_tool, web_search, tool_search, weather_fetch, and visualize:show_widget. No list changes from 4.6, but tool descriptions (unpublished by Anthropic) are key for maximizing chat UI capabilities.",{"title":51,"searchDepth":52,"depth":52,"links":53},"",2,[54,55,56,57],{"id":19,"depth":52,"text":20},{"id":31,"depth":52,"text":32},{"id":38,"depth":52,"text":39},{"id":45,"depth":52,"text":46},[],null,"md",false,{"content_references":63,"triage":86},[64,70,73,77,80,83],{"type":65,"title":66,"author":67,"url":68,"context":69},"other","Claude system prompts","Anthropic","https:\u002F\u002Fplatform.claude.com\u002Fdocs\u002Fen\u002Frelease-notes\u002Fsystem-prompts","cited",{"type":65,"title":71,"author":67,"url":72,"context":69},"system-prompts.md","https:\u002F\u002Fplatform.claude.com\u002Fdocs\u002Fen\u002Frelease-notes\u002Fsystem-prompts.md",{"type":65,"title":74,"author":75,"url":76,"context":69},"Git diff between Opus 4.6 and 4.7","Simon Willison","https:\u002F\u002Fgithub.com\u002Fsimonw\u002Fresearch\u002Fcommit\u002F888f21161500cd60b7c92367f9410e311ffcff09",{"type":65,"title":78,"author":67,"url":79,"context":69},"Tool search tool documentation","https:\u002F\u002Fplatform.claude.com\u002Fdocs\u002Fen\u002Fagents-and-tools\u002Ftool-use\u002Ftool-search-tool",{"type":65,"title":81,"author":67,"url":82,"context":69},"Advanced tool use post","https:\u002F\u002Fwww.anthropic.com\u002Fengineering\u002Fadvanced-tool-use",{"type":65,"title":84,"url":85,"context":69},"Claude tools transcript","https:\u002F\u002Fclaude.ai\u002Fshare\u002Fdc1e375e-2213-4afb-ac1b-812d42735a8e",{"relevance":87,"novelty":88,"quality":87,"actionability":52,"composite":89,"reasoning":90},4,3,3.4,"Category: AI & LLMs. The article discusses updates to Claude's system prompts, which directly relates to AI engineering and prompt engineering, addressing specific audience pain points regarding tool use and safety. However, while it provides insights into the changes, it lacks detailed actionable steps for implementing these updates in a practical context.",true,"\u002Fsummaries\u002F86e4ca3c0a4555e4-claude-opus-4-7-prompt-tweaks-boost-safety-and-too-summary","2026-04-19 01:22:46",{"title":5,"description":51},{"loc":92},"86e4ca3c0a4555e4","Simon Willison's Weblog","article","https:\u002F\u002Fsimonwillison.net\u002F2026\u002FApr\u002F18\u002Fopus-system-prompt\u002F#atom-everything","summaries\u002F86e4ca3c0a4555e4-claude-opus-4-7-prompt-tweaks-boost-safety-and-too-summary",[102,103,104,105],"prompt-engineering","claude","anthropic","system-prompts","Opus 4.7 refines Claude's system prompt to prioritize tool calls over questions, expand child safety refusals across conversations, enforce conciseness, and add guards against disordered eating advice or forced yes\u002Fno on controversies.",[103,104,105],"_w0pnC-ZcHuRtLuUd0qHuS9lU2DIEg2Lm09rgveUdF0",[110,113,116,119,122,125,127,129,131,133,135,137,140,142,144,146,148,150,152,154,156,158,161,164,166,168,171,173,175,178,180,182,184,186,188,190,192,194,196,198,200,202,204,206,208,210,212,214,216,218,220,222,224,226,228,230,232,234,236,238,240,242,244,246,248,250,252,254,256,258,260,262,264,266,268,270,272,274,276,278,280,282,284,286,288,290,292,294,296,298,300,302,304,306,308,310,312,314,316,318,320,322,324,326,328,330,332,334,336,338,340,342,344,346,348,350,352,354,356,358,360,362,364,366,368,370,372,374,376,378,380,382,384,386,388,390,392,394,396,398,400,402,404,406,408,410,412,414,416,418,420,422,424,426,428,430,433,435,437,439,441,443,445,447,449,451,453,455,457,459,461,463,465,467,469,471,473,475,477,479,481,483,485,487,489,491,493,495,497,499,501,503,505,507,509,511,513,515,517,519,521,523,525,527,529,531,533,535,537,539,541,543,545,547,549,551,553,555,557,559,561,563,565,567,569,571,573,575,577,579,581,583,585,587,589,591,593,595,597,599,601,603,605,607,609,611,613,615,617,619,621,623,625,627,629,631,633,635,637,639,641,643,645,647,649,651,653,655,657,659,661,663,665,667,669,671,673,675,677,679,681,683,685,687,689,691,693,695,697,699,701,703,705,707,709,711,713,715,717,719,721,723,725,727,729,731,733,735,737,739,741,743,745,747,749,751,753,755,757,759,761,763,765,767,769,771,773,775,777,779,781,783,785,787,789,791,793,795,797,799,801,803,805,807,809,811,813,815,817,819,821,823,825,827,829,831,833,835,837,839,841,843,845,847,849,851,853,855,857,859,861,863,865,867,869,871,873,875,877,879,881,883,885,887,889,891,893,895,897,899,901,903,905,907,909,911,913,915,917,919,921,923,925,927,929,931,933,935,937,939,941,943,945,947,949,951,953,955,957,959,961,963,965,967,969,971,973,975,977,979,981,983,985,987,989,991,993,995,997,999,1001,1003,1005,1007,1009,1011,1013,1015,1017,1019,1021,1023,1025,1027,1029,1031,1033,1035,1037,1039,1041,1043,1045,1047,1049,1051,1053,1055,1057,1059,1061,1063,1065,1067,1069,1071,1073,1075,1077,1079,1081,1083,1085,1087,1089,1091,1093,1095,1097,1099,1101,1103,1105,1107,1109,1111,1113,1115,1117,1119,1121,1123,1125,1127,1129,1131,1133,1135,1137,1139,1141,1143,1145,1147,1149,1151,1153,1155,1157,1159,1161,1163,1165,1167,1169,1171,1173,1175,1177,1179,1181,1183,1185,1187,1189,1191,1193,1195,1197,1199,1201,1203,1205,1207,1209,1211,1213,1215,1217,1219,1221,1223,1225,1227,1229,1231,1233,1235,1237,1239,1241,1243,1245,1247,1249,1251,1253,1255,1257,1259,1261,1263,1265,1267,1269,1271,1273,1275,1277,1279,1281,1283,1285,1287,1289,1291,1293,1295,1297,1299,1301,1303,1305,1307,1309,1311,1313,1315,1317,1319,1321,1323,1325,1327,1329,1331,1333,1335,1337,1339,1341,1343,1345,1347,1349,1351,1353,1355,1357,1359,1361,1363,1365,1367,1369,1371,1373,1375,1377,1379,1381,1383,1385,1387,1389,1391,1393,1395,1397,1399,1401,1403,1405,1407,1409,1411,1413,1415,1417,1419,1421,1423,1425,1427,1429,1431,1433,1435,1437,1439,1441,1443,1445,1447,1449,1451,1453,1455,1457,1459,1461,1463,1465,1467,1469,1471,1473,1475,1477,1479,1481,1483,1485,1487,1489,1491,1493,1495,1497,1499,1501,1503,1505,1507,1509,1511,1513,1515,1517,1519,1521,1523,1525,1527,1529,1531,1533,1535,1537,1539,1541,1543,1545,1547,1549,1551,1553,1555,1557,1559,1561,1563,1565,1567,1569,1571,1573,1575,1577,1579,1581,1583,1585,1587,1589,1591,1593,1595,1597,1599,1601,1603,1605,1607,1609,1611,1613,1615,1617,1619,1621,1623,1625,1627,1629,1631,1633,1635,1637,1639,1641,1643,1645,1647,1649,1651,1653,1655,1657,1659,1661,1663,1665,1667,1669,1671,1673,1675,1677,1679,1681,1683,1685,1687,1689,1691,1693,1695,1697,1699,1701,1703,1705,1707,1709,1711,1713,1715,1717,1719,1721,1723,1725,1727,1729,1731,1733,1735,1737,1739,1741,1743,1745,1747,1749,1751,1753,1755,1757,1759,1761,1763,1765,1767,1769,1771,1773,1775,1777,1779,1781,1783,1785,1787,1789,1791,1793,1795,1797,1799,1801,1803,1805,1807,1809,1811,1813,1815,1817,1819,1821,1823,1825,1827,1829,1831,1833,1835,1837,1839,1841,1843,1845,1847,1849,1851,1853,1855,1857,1859,1861,1863,1865,1867,1869,1871,1873,1875,1877,1879,1881,1883,1885,1887,1889,1891,1893,1895,1897,1899,1901,1903,1905,1907,1909,1911,1913,1915,1917,1919,1921,1923,1925,1927,1929,1931,1933,1935,1937,1939,1941,1943,1945,1947,1949,1951,1953,1955,1957,1959,1961,1963,1965,1967,1969,1971,1973,1975,1977,1979,1981,1983,1985,1987,1989,1991,1993,1995,1997,1999,2001,2003,2005,2007,2009,2011,2013,2015,2017,2019,2021,2023,2025,2027,2029,2031,2033,2035,2037,2039,2041,2043,2045,2047,2049,2051,2053,2055,2057,2059,2061,2063,2065,2067,2069,2071,2073,2075,2077,2079,2081,2083,2085,2087,2089,2091,2093,2095,2097,2099,2101,2103,2105,2107,2109,2111,2113,2115,2117,2119,2121,2123,2125,2127,2129,2131,2133,2135,2137,2139,2141,2143,2145,2147,2149,2151,2153,2155,2157,2159,2161,2163,2165,2167,2169,2171,2173,2175,2177,2179,2181,2183,2185,2187,2189,2191,2193,2195,2197,2199,2201,2203,2205,2207,2209,2211,2213,2215,2217,2219,2221,2223,2225,2227,2229,2231,2233,2235,2237,2239,2241,2243,2245,2247,2249,2251,2253,2255,2257,2259,2261,2263,226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To analyze evolutions, split it into separate files per model (e.g., Opus), family, and revision using Claude Code. Assign fake git commit dates matching prompt timestamps. This repo structure—",[3698,3699,3703],"a",{"href":3700,"rel":3701},"https:\u002F\u002Fgithub.com\u002Fsimonw\u002Fresearch\u002Ftree\u002Fmain\u002Fextract-system-prompts%E2%80%94turns",[3702],"nofollow","https:\u002F\u002Fgithub.com\u002Fsimonw\u002Fresearch\u002Ftree\u002Fmain\u002Fextract-system-prompts—turns"," history into a queryable timeline, avoiding manual parsing of the  monolithic source.",[22,3706,3707,3708,3712,3713,3716,3717,3720],{},"Commit each prompt version as a granular file, enabling GitHub's commit view for visual browsing. Git operations reveal precise change attribution: ",[3709,3710,3711],"code",{},"git log"," lists evolution chronologically, ",[3709,3714,3715],{},"git diff"," highlights additions\u002Fdeletions between versions like Opus 4.6 and 4.7, and ",[3709,3718,3719],{},"git blame"," pins modifications to exact dates.",[17,3722,3724],{"id":3723},"leverage-git-for-prompt-analysis-trade-offs","Leverage Git for Prompt Analysis Trade-offs",[22,3726,3727],{},"This approach excels for researchers tracking LLM behavior shifts, as prompts directly influence outputs—e.g., comparing 4.6 to 4.7 exposed targeted tweaks without sifting raw Markdown. Trade-off: Fake commits require upfront scripting but unlock native git tooling over ad-hoc diffs. Readers can fork the repo to apply the same workflow to other providers' prompt histories, accelerating reverse-engineering of model updates.",[17,3729,3731],{"id":3730},"real-world-output-opus-46-to-47-insights","Real-world Output: Opus 4.6 to 4.7 Insights",[22,3733,3734,3735,3739],{},"Applied to Opus changes, git diffs surfaced specific refinements, fueling detailed notes at ",[3698,3736,3737],{"href":3737,"rel":3738},"https:\u002F\u002Fsimonwillison.net\u002F2026\u002FApr\u002F18\u002Fopus-system-prompt\u002F",[3702],". This proves the method's value: from raw docs to actionable insights in minutes, versus hours of manual review.",{"title":51,"searchDepth":52,"depth":52,"links":3741},[3742,3743,3744],{"id":3692,"depth":52,"text":3693},{"id":3723,"depth":52,"text":3724},{"id":3730,"depth":52,"text":3731},[],{"content_references":3747,"triage":3759},[3748,3750,3752,3757],{"type":65,"title":3749,"url":68,"context":69},"System prompts for Claude chat",{"type":65,"title":3751,"url":72,"context":69},"System prompts Markdown",{"type":3753,"title":3754,"url":3755,"context":3756},"tool","extract-system-prompts","https:\u002F\u002Fgithub.com\u002Fsimonw\u002Fresearch\u002Ftree\u002Fmain\u002Fextract-system-prompts#readme","mentioned",{"type":65,"title":3758,"url":3737,"context":3756},"Changes in the system prompt between Claude Opus 4.6 and 4.7",{"relevance":3760,"novelty":87,"quality":87,"actionability":87,"composite":3761,"reasoning":3762},5,4.35,"Category: AI & LLMs. The article provides a practical method for analyzing changes in LLM prompts using Git, directly addressing the audience's need for actionable insights in AI product development. It offers a novel approach to prompt analysis that can be immediately applied by developers and researchers.","\u002Fsummaries\u002F0d500956cacf6768-claude-system-prompts-as-git-timeline-for-diffing-summary",{"title":3682,"description":51},{"loc":3763},"0d500956cacf6768","https:\u002F\u002Fsimonwillison.net\u002F2026\u002FApr\u002F18\u002Fextract-system-prompts\u002F#atom-everything","summaries\u002F0d500956cacf6768-claude-system-prompts-as-git-timeline-for-diffing--summary",[3770,102,103],"llm","Convert Anthropic's monolithic Claude system prompts Markdown into per-model git files with fake commits to use git log\u002Fdiff\u002Fblame for tracing changes by date and revision.",[103],"UBIh4jPGIIxAaeCKJlg8c1-LbuF8VClO7hzB2iyJJ50",{"id":3775,"title":3776,"ai":3777,"body":3782,"categories":3810,"created_at":59,"date_modified":59,"description":51,"extension":60,"faq":59,"featured":61,"kicker_label":59,"meta":3811,"navigation":91,"path":3823,"published_at":3824,"question":59,"scraped_at":3825,"seo":3826,"sitemap":3827,"source_id":3828,"source_name":3829,"source_type":98,"source_url":3830,"stem":3831,"tags":3832,"thumbnail_url":59,"tldr":3833,"tweet":59,"unknown_tags":3834,"__hash__":3835},"summaries\u002Fsummaries\u002F7bfce2f937233fa5-pre-mortem-prompts-fix-claude-s-yes-man-bias-summary.md","Pre-Mortem Prompts Fix Claude's Yes-Man Bias",{"provider":7,"model":8,"input_tokens":3778,"output_tokens":3779,"processing_time_ms":3780,"cost_usd":3781},3893,1649,24045,0.00109625,{"type":14,"value":3783,"toc":3805},[3784,3788,3791,3795,3798,3802],[17,3785,3787],{"id":3786},"bypass-rlhf-flattery-with-failure-framing","Bypass RLHF Flattery with Failure Framing",[22,3789,3790],{},"Claude's RLHF training makes it overly agreeable, turning 'Is this a good plan?' into superficial praise disguised as critique. This wastes time since it rarely flags real flaws. Instead, frame prompts around inevitable failure: tell Claude your plan died 6 months from now and ask it to narrate the autopsy. This inverts bias, surfacing hidden risks like market shifts, execution gaps, or overlooked dependencies that affirmative prompts ignore. Readers gain production-ready critiques, turning vague agreement into actionable fixes.",[17,3792,3794],{"id":3793},"execute-the-pre-mortem-technique","Execute the Pre-Mortem Technique",[22,3796,3797],{},"Start by stating the plan's failure as fact—'Six months from now, this project failed completely. Explain exactly how.' Claude then generates plausible failure paths, such as technical debt accumulation or user drop-off from poor UX. Use outputs to iterate: patch the top 3-5 risks before relaunch. This method delivers what standard prompts bury, providing concrete mitigation steps. For AI product builders, it accelerates from idea to robust prototype by preempting derailments.",[17,3799,3801],{"id":3800},"proven-origins-and-impact","Proven Origins and Impact",[22,3803,3804],{},"Gary Klein invented the pre-mortem in 1989 for high-stakes decisions; Daniel Kahneman later called it his most valuable tool for avoiding overconfidence. Applied to LLMs, it leverages Claude's narrative strengths without affirmation traps, yielding deeper insights than yes\u002Fno evaluations. Builders testing this report 2-3x sharper risk identification, directly improving plan survival odds in competitive AI landscapes.",{"title":51,"searchDepth":52,"depth":52,"links":3806},[3807,3808,3809],{"id":3786,"depth":52,"text":3787},{"id":3793,"depth":52,"text":3794},{"id":3800,"depth":52,"text":3801},[],{"content_references":3812,"triage":3820},[3813,3817],{"type":65,"title":3814,"author":3815,"publisher":3816,"context":69},"Pre-mortem","Klein","1989",{"type":65,"title":3814,"author":3818,"context":3819},"Kahneman","recommended",{"relevance":3760,"novelty":87,"quality":87,"actionability":3760,"composite":3821,"reasoning":3822},4.55,"Category: AI & LLMs. The article provides a practical technique for overcoming biases in AI models, specifically Claude, which is highly relevant for AI product builders. It offers a concrete method for risk analysis that can be immediately applied to improve product planning and execution.","\u002Fsummaries\u002F7bfce2f937233fa5-pre-mortem-prompts-fix-claude-s-yes-man-bias-summary","2026-05-08 06:49:18","2026-05-09 15:36:43",{"title":3776,"description":51},{"loc":3823},"7bfce2f937233fa5","Generative AI","https:\u002F\u002Fgenerativeai.pub\u002Fthe-pre-mortem-trick-that-makes-claude-absolutely-great-630f610809d6?source=rss----440100e76000---4","summaries\u002F7bfce2f937233fa5-pre-mortem-prompts-fix-claude-s-yes-man-bias-summary",[102,3770],"Claude flatters plans due to RLHF; prompt it to assume failure in 6 months and explain why to get honest risk analysis—Kahneman's top decision tool, invented by Klein in 1989.",[],"_JRWh85QpktDTwWGwKH6-1DgPKMCYLq-bnFSVrwdo-E",{"id":3837,"title":3838,"ai":3839,"body":3844,"categories":4193,"created_at":59,"date_modified":59,"description":51,"extension":60,"faq":59,"featured":61,"kicker_label":59,"meta":4194,"navigation":91,"path":4205,"published_at":4206,"question":59,"scraped_at":4207,"seo":4208,"sitemap":4209,"source_id":4210,"source_name":4211,"source_type":98,"source_url":4212,"stem":4213,"tags":4214,"thumbnail_url":59,"tldr":4215,"tweet":59,"unknown_tags":4216,"__hash__":4217},"summaries\u002Fsummaries\u002Fb0d82d6ef098f216-guarantee-llm-outputs-match-exact-taxonomies-with--summary.md","Guarantee LLM Outputs Match Exact Taxonomies with Tries",{"provider":7,"model":8,"input_tokens":3840,"output_tokens":3841,"processing_time_ms":3842,"cost_usd":3843},7679,2345,26271,0.0026858,{"type":14,"value":3845,"toc":4188},[3846,3850,3853,3860,3863,3887,3891,3894,4065,4072,4079,4154,4161,4165,4172,4175,4178,4181,4184],[17,3847,3849],{"id":3848},"logit-masking-guarantees-valid-outputs","Logit Masking Guarantees Valid Outputs",[22,3851,3852],{},"LLMs generate tokens autoregressively, producing a logit vector over 32,000-100,000 vocabulary tokens at each step, converted to probabilities via softmax. Any token with finite logit has nonzero probability, allowing hallucinations like near-miss labels (e.g., \"Techology\" instead of \"Technology\"). Standard fixes—prompt instructions, string matching, retries—fail because they act post-generation.",[22,3854,3855,3856,3859],{},"Constrained decoding intervenes pre-sampling: set logits of invalid tokens to -∞, yielding exactly zero softmax probability. Remaining valid logits renormalize to sum to 1. This works for any sampling (greedy, temperature, top-p, top-k) since zero-probability tokens cannot be selected. In code: ",[3709,3857,3858],{},"logits[~valid_token_mask] = float('-inf')",".",[22,3861,3862],{},"Validity depends on prior tokens. A trie (prefix tree) encodes all taxonomy labels as token paths. Root children are first tokens of any label; deeper nodes narrow to continuations. After prefix \" Tech\" (token ID 8987), only \"nology\" (ID 1366) is valid. At end nodes, only EOS is valid, terminating the label.",[22,3864,3865,3866,3869,3870,3873,3874,3878,3879,3882,3883,3886],{},"Tokenization nuance: BPE splits depend on context. Tokenize labels as continuations with leading space (",[3709,3867,3868],{},"\" \" + label",", ",[3709,3871,3872],{},"add_special_tokens=False","), e.g., Qwen2.5 tokenizes \" Sports\" to ",[3875,3876,3877],"span",{},"22470",", not \"Sports\" to ",[3875,3880,3881],{},"51660",". Verify round-trip: ",[3709,3884,3885],{},"tokenizer.decode(token_ids) == \" \" + label",". Tiktoken (GPT-4 family) bakes whitespace into boundaries without ▁.",[17,3888,3890],{"id":3889},"trie-and-logits-processor-implementation","Trie and Logits Processor Implementation",[22,3892,3893],{},"Build trie from labels:",[3895,3896,3900],"pre",{"className":3897,"code":3898,"language":3899,"meta":51,"style":51},"language-python shiki shiki-themes github-light github-dark","class TrieNode:\n    def __init__(self):\n        self.children = {}  # token_id → TrieNode\n        self.is_end = False\n\nclass ConstrainedTrie:\n    def __init__(self):\n        self.root = TrieNode()\n    def insert(self, token_ids):\n        node = self.root\n        for tid in token_ids:\n            if tid not in node.children:\n                node.children[tid] = TrieNode()\n            node = node.children[tid]\n        node.is_end = True\n    def get_valid_next_tokens(self, prefix):\n        node = self.root\n        for tid in prefix:\n            if tid not in node.children:\n                return set()\n            node = node.children[tid]\n        return set(node.children.keys())\n    def is_complete(self, prefix):\n        node = self.root\n        for tid in prefix:\n            if tid not in node.children:\n                return False\n            node = node.children[tid]\n        return node.is_end\n","python",[3709,3901,3902,3909,3914,3919,3924,3929,3935,3940,3946,3952,3958,3964,3970,3976,3982,3988,3994,3999,4005,4010,4016,4021,4027,4033,4038,4043,4048,4054,4059],{"__ignoreMap":51},[3875,3903,3906],{"class":3904,"line":3905},"line",1,[3875,3907,3908],{},"class TrieNode:\n",[3875,3910,3911],{"class":3904,"line":52},[3875,3912,3913],{},"    def __init__(self):\n",[3875,3915,3916],{"class":3904,"line":88},[3875,3917,3918],{},"        self.children = {}  # token_id → TrieNode\n",[3875,3920,3921],{"class":3904,"line":87},[3875,3922,3923],{},"        self.is_end = False\n",[3875,3925,3926],{"class":3904,"line":3760},[3875,3927,3928],{"emptyLinePlaceholder":91},"\n",[3875,3930,3932],{"class":3904,"line":3931},6,[3875,3933,3934],{},"class ConstrainedTrie:\n",[3875,3936,3938],{"class":3904,"line":3937},7,[3875,3939,3913],{},[3875,3941,3943],{"class":3904,"line":3942},8,[3875,3944,3945],{},"        self.root = TrieNode()\n",[3875,3947,3949],{"class":3904,"line":3948},9,[3875,3950,3951],{},"    def insert(self, token_ids):\n",[3875,3953,3955],{"class":3904,"line":3954},10,[3875,3956,3957],{},"        node = self.root\n",[3875,3959,3961],{"class":3904,"line":3960},11,[3875,3962,3963],{},"        for tid in token_ids:\n",[3875,3965,3967],{"class":3904,"line":3966},12,[3875,3968,3969],{},"            if tid not in node.children:\n",[3875,3971,3973],{"class":3904,"line":3972},13,[3875,3974,3975],{},"                node.children[tid] = TrieNode()\n",[3875,3977,3979],{"class":3904,"line":3978},14,[3875,3980,3981],{},"            node = node.children[tid]\n",[3875,3983,3985],{"class":3904,"line":3984},15,[3875,3986,3987],{},"        node.is_end = True\n",[3875,3989,3991],{"class":3904,"line":3990},16,[3875,3992,3993],{},"    def get_valid_next_tokens(self, prefix):\n",[3875,3995,3997],{"class":3904,"line":3996},17,[3875,3998,3957],{},[3875,4000,4002],{"class":3904,"line":4001},18,[3875,4003,4004],{},"        for tid in prefix:\n",[3875,4006,4008],{"class":3904,"line":4007},19,[3875,4009,3969],{},[3875,4011,4013],{"class":3904,"line":4012},20,[3875,4014,4015],{},"                return set()\n",[3875,4017,4019],{"class":3904,"line":4018},21,[3875,4020,3981],{},[3875,4022,4024],{"class":3904,"line":4023},22,[3875,4025,4026],{},"        return set(node.children.keys())\n",[3875,4028,4030],{"class":3904,"line":4029},23,[3875,4031,4032],{},"    def is_complete(self, prefix):\n",[3875,4034,4036],{"class":3904,"line":4035},24,[3875,4037,3957],{},[3875,4039,4041],{"class":3904,"line":4040},25,[3875,4042,4004],{},[3875,4044,4046],{"class":3904,"line":4045},26,[3875,4047,3969],{},[3875,4049,4051],{"class":3904,"line":4050},27,[3875,4052,4053],{},"                return False\n",[3875,4055,4057],{"class":3904,"line":4056},28,[3875,4058,3981],{},[3875,4060,4062],{"class":3904,"line":4061},29,[3875,4063,4064],{},"        return node.is_end\n",[22,4066,4067,4068,4071],{},"Insert: ",[3709,4069,4070],{},"token_ids = tokenizer.encode(\" \" + label, add_special_tokens=False); trie.insert(token_ids)",". Rebuild on taxonomy changes (milliseconds for hundreds-thousands labels).",[22,4073,4074,4075,4078],{},"HuggingFace ",[3709,4076,4077],{},"LogitsProcessor",":",[3895,4080,4082],{"className":3897,"code":4081,"language":3899,"meta":51,"style":51},"class TrieLogitsProcessor(LogitsProcessor):\n    def __init__(self, trie, prompt_length, eos_token_id):\n        self.trie = trie\n        self.prompt_length = prompt_length\n        self.eos = eos_token_id\n    def __call__(self, input_ids, scores):\n        generated = input_ids[0, self.prompt_length:].tolist()\n        valid = self.trie.get_valid_next_tokens(generated)\n        if self.trie.is_complete(generated):\n            valid.add(self.eos)\n        masked = torch.full_like(scores, float('-inf'))\n        for tid in valid:\n            masked[0, tid] = scores[0, tid]\n        return masked\n",[3709,4083,4084,4089,4094,4099,4104,4109,4114,4119,4124,4129,4134,4139,4144,4149],{"__ignoreMap":51},[3875,4085,4086],{"class":3904,"line":3905},[3875,4087,4088],{},"class TrieLogitsProcessor(LogitsProcessor):\n",[3875,4090,4091],{"class":3904,"line":52},[3875,4092,4093],{},"    def __init__(self, trie, prompt_length, eos_token_id):\n",[3875,4095,4096],{"class":3904,"line":88},[3875,4097,4098],{},"        self.trie = trie\n",[3875,4100,4101],{"class":3904,"line":87},[3875,4102,4103],{},"        self.prompt_length = prompt_length\n",[3875,4105,4106],{"class":3904,"line":3760},[3875,4107,4108],{},"        self.eos = eos_token_id\n",[3875,4110,4111],{"class":3904,"line":3931},[3875,4112,4113],{},"    def __call__(self, input_ids, scores):\n",[3875,4115,4116],{"class":3904,"line":3937},[3875,4117,4118],{},"        generated = input_ids[0, self.prompt_length:].tolist()\n",[3875,4120,4121],{"class":3904,"line":3942},[3875,4122,4123],{},"        valid = self.trie.get_valid_next_tokens(generated)\n",[3875,4125,4126],{"class":3904,"line":3948},[3875,4127,4128],{},"        if self.trie.is_complete(generated):\n",[3875,4130,4131],{"class":3904,"line":3954},[3875,4132,4133],{},"            valid.add(self.eos)\n",[3875,4135,4136],{"class":3904,"line":3960},[3875,4137,4138],{},"        masked = torch.full_like(scores, float('-inf'))\n",[3875,4140,4141],{"class":3904,"line":3966},[3875,4142,4143],{},"        for tid in valid:\n",[3875,4145,4146],{"class":3904,"line":3972},[3875,4147,4148],{},"            masked[0, tid] = scores[0, tid]\n",[3875,4150,4151],{"class":3904,"line":3978},[3875,4152,4153],{},"        return masked\n",[22,4155,4156,4157,4160],{},"Generate: ",[3709,4158,4159],{},"model.generate(input_ids, logits_processor=LogitsProcessorList([processor]), max_new_tokens=16)",". Output decodes to exact label.",[17,4162,4164],{"id":4163},"multi-label-hierarchies-and-broader-applications","Multi-Label, Hierarchies, and Broader Applications",[22,4166,4167,4168,4171],{},"For multi-label: After end node, allow EOS or separator (e.g., ",[3709,4169,4170],{},"|,|","). Parse generated tokens into seen labels and current prefix. At root, exclude first tokens only after all labels sharing it are emitted (precompute groups by first token). Supports hierarchies: insert full paths like \"Technology > AI > NLP\"; shared prefixes compress naturally.",[22,4173,4174],{},"Edge cases: Low confidence concentrates mass on valid tokens (fix: fine-tune); long labels create narrow paths (fine-tune improves); rebuild trie on changes.",[22,4176,4177],{},"Proof of correctness: (1) Forward invariant—emitted tokens always extend valid prefixes; (2) Termination invariant—EOS only at end nodes. Verify by enumerating trie paths against labels. Independent of model, temperature, etc.",[22,4179,4180],{},"Limitations: Needs logit access (open models like Qwen2.5, not OpenAI APIs); masking redistributes probability (structurally correct but semantically wrong possible); no accuracy boost—pair with fine-tuning.",[22,4182,4183],{},"Generalizes to JSON (trie encodes schema), SQL (grammar FSM), agents (tool names). Enforces structure without prompt\u002Fmodel changes.",[4185,4186,4187],"style",{},"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 .dark .shiki span {color: var(--shiki-dark);background: var(--shiki-dark-bg);font-style: var(--shiki-dark-font-style);font-weight: var(--shiki-dark-font-weight);text-decoration: var(--shiki-dark-text-decoration);}html.dark .shiki span {color: var(--shiki-dark);background: var(--shiki-dark-bg);font-style: var(--shiki-dark-font-style);font-weight: var(--shiki-dark-font-weight);text-decoration: var(--shiki-dark-text-decoration);}",{"title":51,"searchDepth":52,"depth":52,"links":4189},[4190,4191,4192],{"id":3848,"depth":52,"text":3849},{"id":3889,"depth":52,"text":3890},{"id":4163,"depth":52,"text":4164},[118],{"content_references":4195,"triage":4203},[4196,4199],{"type":3753,"title":4197,"url":4198,"context":3819},"constrained-decoding","https:\u002F\u002Fgithub.com\u002FSachinKalsi\u002Fconstrained-decoding",{"type":65,"title":4200,"author":4201,"url":4202,"context":69},"Why do we use negative infinity for masking in attention","Sachin Kalsi","https:\u002F\u002Fmedium.com\u002F@sachinkalsi\u002Fwhy-do-we-use-negative-infinity-for-masking-in-attention-450c59274ac8",{"relevance":3760,"novelty":87,"quality":87,"actionability":87,"composite":3761,"reasoning":4204},"Category: AI & LLMs. The article provides a detailed method for constraining LLM outputs to match specific taxonomies, addressing a key pain point for developers integrating AI features. It includes practical code examples and a clear explanation of the trie data structure, making it actionable for the audience.","\u002Fsummaries\u002Fb0d82d6ef098f216-guarantee-llm-outputs-match-exact-taxonomies-with-summary","2026-05-07 04:37:46","2026-05-07 11:23:51",{"title":3838,"description":51},{"loc":4205},"b0d82d6ef098f216","Towards AI","https:\u002F\u002Fpub.towardsai.net\u002Fconstrained-decoding-forcing-llms-to-respect-your-taxonomy-3aaaf13329f9?source=rss----98111c9905da---4","summaries\u002Fb0d82d6ef098f216-guarantee-llm-outputs-match-exact-taxonomies-with--summary",[3770,102],"Constrain LLM generation by masking invalid logits to -∞ using a trie of tokenized labels, ensuring outputs are always exact taxonomy matches regardless of sampling method.",[],"Zl9RXUbRJ9rGvj_m9MKVvEJnqmRWIclKKfYIxgiJrns",{"id":4219,"title":4220,"ai":4221,"body":4226,"categories":4318,"created_at":59,"date_modified":59,"description":51,"extension":60,"faq":59,"featured":61,"kicker_label":59,"meta":4319,"navigation":91,"path":4323,"published_at":4324,"question":59,"scraped_at":4325,"seo":4326,"sitemap":4327,"source_id":4328,"source_name":4329,"source_type":98,"source_url":4330,"stem":4331,"tags":4332,"thumbnail_url":59,"tldr":4334,"tweet":59,"unknown_tags":4335,"__hash__":4336},"summaries\u002Fsummaries\u002F96b13f6e6afc89b5-ai-agent-teams-roles-like-doers-planners-critics-summary.md","AI Agent Teams: Roles Like Doers, Planners, Critics",{"provider":7,"model":8,"input_tokens":4222,"output_tokens":4223,"processing_time_ms":4224,"cost_usd":4225},5215,1195,7433,0.00161785,{"type":14,"value":4227,"toc":4313},[4228,4232,4264,4267,4271,4290,4294],[17,4229,4231],{"id":4230},"core-roles-mirror-human-teams-for-complex-tasks","Core Roles Mirror Human Teams for Complex Tasks",[22,4233,4234,4235,4239,4240,4243,4244,4247,4248,4251,4252,4255,4256,4259,4260,4263],{},"AI agents tackle problems beyond single LLMs by dividing labor into subagents with distinct roles, just as human teams do for projects like mobile app development. Start with a ",[4236,4237,4238],"strong",{},"doer"," for granular actions like coding individual steps. Add a ",[4236,4241,4242],{},"planner"," to decompose user input into requirements and architecture plans, identifying needed skills. Include a ",[4236,4245,4246],{},"tool operator"," for API calls, Python snippets, or web services with structured inputs\u002Foutputs. A ",[4236,4249,4250],{},"learner"," pulls external data via RAG or rules-based retrieval, like competitor app features from blogs\u002Fsocial media, to inform planning. Deploy a ",[4236,4253,4254],{},"critic"," for blunt feedback: hallucination checks, QA tests, or scoring rival outputs for the best one. Use a ",[4236,4257,4258],{},"supervisor"," to monitor progress at task\u002Fproject levels, unsticking stalled steps. End with a ",[4236,4261,4262],{},"presenter"," to synthesize outputs, summarizing requirements, code, and results for users.",[22,4265,4266],{},"These roles scale from simple to robust: tool operators and learners often chain LLM calls with tools\u002Fretrieval, forming standalone agents themselves.",[17,4268,4270],{"id":4269},"react-pattern-as-starter-team-expand-for-reliability","ReAct Pattern as Starter Team, Expand for Reliability",[22,4272,4273,4274,4277,4278,4281,4282,4285,4286,4289],{},"Combine roles into proven patterns like ReAct: ",[4236,4275,4276],{},"reason"," (planner breaks down tasks), ",[4236,4279,4280],{},"act"," (tool operator executes), ",[4236,4283,4284],{},"observe"," (critic feedbacks), yielding a final ",[4236,4287,4288],{},"answer"," (presenter). This handles basic loops but falters on diverse\u002Fcomplex tasks. Scale by adding roles for deeper planning, precise execution, and internal feedback, boosting output quality—like growing a startup team to fix bugs and polish products.",[17,4291,4293],{"id":4292},"optimize-roles-with-prompting-models-tuning-context","Optimize Roles with Prompting, Models, Tuning, Context",[22,4295,4296,4297,4300,4301,4304,4305,4308,4309,4312],{},"Excel roles via four levers: (1) ",[4236,4298,4299],{},"Prompting"," gives clear instructions, e.g., 'retry if stuck,' mirroring human guidance. (2) ",[4236,4302,4303],{},"Model selection"," matches role needs—specialization, size, reasoning ability, persona (e.g., analytical critic). (3) ",[4236,4306,4307],{},"Model tuning"," feeds good\u002Fbad examples to fine-tune weights, but demands datasets and compute. (4) ",[4236,4310,4311],{},"Context"," provides targeted access (files, DBs, APIs) without overload, like onboarding humans. Begin lean with 2-3 roles for quick prototypes, then expand to cover weaknesses.",{"title":51,"searchDepth":52,"depth":52,"links":4314},[4315,4316,4317],{"id":4230,"depth":52,"text":4231},{"id":4269,"depth":52,"text":4270},{"id":4292,"depth":52,"text":4293},[],{"content_references":4320,"triage":4321},[],{"relevance":3760,"novelty":87,"quality":87,"actionability":87,"composite":3761,"reasoning":4322},"Category: AI & LLMs. The article provides a detailed framework for building AI agent teams by assigning specific roles, which directly addresses the audience's need for practical applications in AI integration. It offers actionable insights on optimizing these roles through prompting and model selection, making it highly relevant for product builders.","\u002Fsummaries\u002F96b13f6e6afc89b5-ai-agent-teams-roles-like-doers-planners-critics-summary","2026-04-21 11:01:16","2026-04-21 15:13:37",{"title":4220,"description":51},{"loc":4323},"7cb95c72ba265a1e","IBM Technology","https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=kqj22mWIdjU","summaries\u002F96b13f6e6afc89b5-ai-agent-teams-roles-like-doers-planners-critics-summary",[4333,102],"agents","Build AI agents for complex tasks by assigning specialized subagent roles—doers for execution, planners for breakdown, critics for feedback—like human teams, then optimize via prompting, model selection, tuning, and context.",[],"kidHdGccgH6AfhKxBUfjDt5-K-AEWf0QR9FInn623es"]