[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"summary-d009bef297bc0ca2-fix-ai-note-forgetting-unlock-llm-mechanics-via-ra-summary":3,"summaries-facets-categories":101,"summary-related-d009bef297bc0ca2-fix-ai-note-forgetting-unlock-llm-mechanics-via-ra-summary":3671},{"id":4,"title":5,"ai":6,"body":13,"categories":72,"created_at":73,"date_modified":73,"description":65,"extension":74,"faq":73,"featured":75,"kicker_label":73,"meta":76,"navigation":83,"path":84,"published_at":85,"question":73,"scraped_at":86,"seo":87,"sitemap":88,"source_id":89,"source_name":90,"source_type":91,"source_url":92,"stem":93,"tags":94,"thumbnail_url":73,"tldr":98,"tweet":73,"unknown_tags":99,"__hash__":100},"summaries\u002Fsummaries\u002Fd009bef297bc0ca2-fix-ai-note-forgetting-unlock-llm-mechanics-via-ra-summary.md","Fix AI Note Forgetting: Unlock LLM Mechanics via RAG",{"provider":7,"model":8,"input_tokens":9,"output_tokens":10,"processing_time_ms":11,"cost_usd":12},"openrouter","x-ai\u002Fgrok-4.1-fast",6719,1381,18517,0.00201195,{"type":14,"value":15,"toc":64},"minimark",[16,21,25,28,31,35,38,41,45,48,51,54,58,61],[17,18,20],"h2",{"id":19},"structure-notes-first-to-enable-reliable-ai-use","Structure Notes First to Enable Reliable AI Use",[22,23,24],"p",{},"Scattered notes across apps like Notion and Google Docs waste time reconstructing context, blocking progress. Consolidate into Markdown files with a fixed pattern: concept header, short explanation, personal analogy, and open questions section. Add metadata like topic and difficulty at the top. This creates predictable input the AI can parse consistently, reducing manual re-entry and making notes scannable even without AI. The key shift: treat notes as a structured collection, not isolated fragments—AI reliability starts with usable input, not model tweaks.",[22,26,27],{},"Shift from chat interfaces to API scripts for automation: load notes programmatically, send with queries, handle API keys securely, and monitor token-based billing to avoid surprise costs. Sending all notes every time works briefly but fails as volume grows due to context window limits—models drop unseen content without warning, causing inconsistent responses.",[22,29,30],{},"Tokens (sub-word chunks) accumulate fast in technical notes; a few pages hit thousands, exceeding limits like 128k for many models. Solution: work within constraints by sending only relevant info, turning AI from unpredictable chat into a buildable component.",[17,32,34],{"id":33},"use-retrieval-to-fit-context-windows-and-boost-consistency","Use Retrieval to Fit Context Windows and Boost Consistency",[22,36,37],{},"Dumping all notes overloads the context window, so search notes first for query-relevant sections, extract those chunks, and feed only them to the model. This retrieval-augmented generation (RAG) grounds responses in your exact wording and analogies, making outputs mirror your thinking without dilution. RAG doesn't make the model smarter—it anchors it to your notes, preventing drift from pretrained knowledge.",[22,39,40],{},"Impact: answers stay consistent across queries, details from notes surface reliably, and token usage drops, cutting costs and fitting larger note collections. Flip from 'send everything' to 'retrieve precisely what's needed'—this scales as notes grow from dozens to hundreds.",[17,42,44],{"id":43},"block-hallucinations-with-explicit-boundaries","Block Hallucinations with Explicit Boundaries",[22,46,47],{},"Even with retrieval, models blend notes with internal knowledge, inventing plausible details like unmentioned formulas (e.g., in backpropagation explanations). Hallucination isn't random error—it's the model helpfully filling gaps to complete responses, blending sources seamlessly so you accept fakes as fact.",[22,49,50],{},"Fix: prefix every prompt with a strict rule: \"Answer using only the provided notes. If info is missing, state clearly 'This isn't covered in your notes' instead of guessing.\" This enforces boundaries, yielding honest responses that flag knowledge gaps—turning limitations into study signals (e.g., 'focus here next').",[22,52,53],{},"Result: responses stick to your intuition-focused notes (no surprise math), build trust through transparency, and clarify what you truly understand versus assumed. Without this, AI blurs personal knowledge lines; with it, it becomes a precise learning mirror.",[17,55,57],{"id":56},"tune-temperature-for-task-specific-outputs","Tune Temperature for Task-Specific Outputs",[22,59,60],{},"One system serves multiple needs: deterministic explanations (repeatable, grounded) versus creative practice questions (varied for testing). Use the temperature parameter—low (e.g., 0.0-0.2) for stable, confident outputs sticking to notes; high (e.g., 0.7+) for diverse phrasings and idea combinations.",[22,62,63],{},"No setup changes needed—just swap values per task. This reveals LLMs as constraint-driven systems: context limits spawn tokens\u002Fretrieval, gap-filling causes hallucinations (fixed by instructions), and output style tunes via params. Hands-on fixes demystify behavior, shifting AI from 'magic' to predictable tool for building study pipelines.",{"title":65,"searchDepth":66,"depth":66,"links":67},"",2,[68,69,70,71],{"id":19,"depth":66,"text":20},{"id":33,"depth":66,"text":34},{"id":43,"depth":66,"text":44},{"id":56,"depth":66,"text":57},[],null,"md",false,{"content_references":77,"triage":78},[],{"relevance":79,"novelty":80,"quality":80,"actionability":79,"composite":81,"reasoning":82},5,4,4.55,"Category: AI & LLMs. The article provides a detailed approach to improving AI note-taking through structured input and retrieval-augmented generation (RAG), addressing practical pain points for developers integrating AI into their workflows. It offers actionable steps like structuring notes in Markdown and using API scripts, making it highly relevant and immediately applicable.",true,"\u002Fsummaries\u002Fd009bef297bc0ca2-fix-ai-note-forgetting-unlock-llm-mechanics-via-ra-summary","2026-05-03 12:01:01","2026-05-03 17:00:57",{"title":5,"description":65},{"loc":84},"d009bef297bc0ca2","Towards AI","article","https:\u002F\u002Fpub.towardsai.net\u002Fai-kept-forgetting-my-notes-fixing-that-taught-me-how-it-actually-works-e08ff209403d?source=rss----98111c9905da---4","summaries\u002Fd009bef297bc0ca2-fix-ai-note-forgetting-unlock-llm-mechanics-via-ra-summary",[95,96,97],"llm","prompt-engineering","ai-automation","Structure notes in consistent Markdown, retrieve relevant chunks to fit context windows (measured in tokens), instruct model to use only provided notes to avoid hallucinations, and tune temperature for consistent explanations or varied practice 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Block responses below thresholds like 0.9583 with a HARD-STOP to enforce alignment to 'Expert DNA'—a gold-standard intent vector from domain pros. Only passing queries reach the Cognitive Reasoning Layer, using greedy decoding (temperature=0.0) for repeatable, non-hallucinated outputs. This Normalized Expert Zone (NEZ) + IGZ + reasoning stack transforms probabilistic LLMs into deterministic systems, ideal for safety-critical ops like Orion ECLSS diagnostics where generic answers risk failure.",[22,3690,3691],{},"Trade-off: High thresholds (0.9583) silence even relevant queries without perfect vector match, prioritizing certainty over flexibility; low ones (0.5) mimic standard assistants but invite drift.",[17,3693,3695],{"id":3694},"fit-70b-model-on-24gb-l4-gpu-with-quantization-and-offloading","Fit 70B Model on 24GB L4 GPU with Quantization and Offloading",[22,3697,3698],{},"Load DeepSeek-R1-Distill-Llama-70B in Q4_K_M GGUF format (42.5 GB compressed) to slash VRAM needs while preserving reasoning. Enable Flash Attention 2 for faster processing, offload 28 layers to GPU (n_gpu_layers=28), and handle the rest on CPU to dodge OOM errors. Result: Sovereign, on-premise inference without cloud leaks, maintaining industrial data privacy.",[22,3700,3701],{},"This setup proves massive models viable on edge hardware—L4's 24GB runs what once needed clusters— but demands tuning layers per workload to balance speed and accuracy.",[17,3703,3705],{"id":3704},"thresholds-deliver-sovereign-agency-with-auditable-certainty","Thresholds Deliver Sovereign Agency with Auditable Certainty",[22,3707,3708],{},"At SROI=0.5, AI details spacecraft oxygen protocols freely; at 0.9583, it rejects near-misses, embodying 'Expert Manifold' confinement. Outputs stay auditable via fixed decoding, ending unsupervised AI in pro environments. H2E shifts from prompt hacks to geometric governance: queries must geometrically align to expert reality or halt, yielding certainty over creativity for high-stakes like aerospace where one hallucination compromises safety.",{"title":65,"searchDepth":66,"depth":66,"links":3710},[3711,3712,3713],{"id":3684,"depth":66,"text":3685},{"id":3694,"depth":66,"text":3695},{"id":3704,"depth":66,"text":3705},[110],{"content_references":3716,"triage":3726},[3717,3722],{"type":3718,"title":3719,"url":3720,"context":3721},"tool","H2E_DS.ipynb","https:\u002F\u002Fgithub.com\u002Ffrank-morales2020\u002FMLxDL\u002Fblob\u002Fmain\u002FH2E_DS.ipynb","mentioned",{"type":3723,"title":3724,"url":3725,"context":3721},"other","The Wall Before the Word: H2E Geometric Governance and the Future of AI Government","https:\u002F\u002Fmedium.com\u002F@frankmorales_91352\u002Fthe-wall-before-the-word-h2e-geometric-governance-and-the-future-of-ai-government-89ff82c7598a",{"relevance":80,"novelty":80,"quality":80,"actionability":3727,"composite":3728,"reasoning":3729},3,3.8,"Category: AI & LLMs. The article discusses the H2E framework for ensuring deterministic outputs from LLMs in high-stakes environments, addressing a specific pain point of needing reliable AI responses. It provides a novel approach to prompt engineering and governance, though it lacks detailed step-by-step guidance for implementation.","\u002Fsummaries\u002F8ac2fb30e2408980-h2e-locks-llms-into-expert-only-responses-via-sema-summary","2026-04-13 09:44:23","2026-04-13 17:53:12",{"title":3674,"description":65},{"loc":3730},"8ac2fb30e2408980","AI Simplified in Plain English","https:\u002F\u002Fmedium.com\u002Fai-simplified-in-plain-english\u002Fthe-architecture-of-certainty-deterministic-governance-in-high-stakes-ai-with-deepseek-9d15e61cc38c?source=rss----f37ab7d4e76b---4","summaries\u002F8ac2fb30e2408980-h2e-locks-llms-into-expert-only-responses-via-sema-summary",[95,96,97],"H2E framework uses cosine similarity (SROI) thresholds like 0.9583 to gate queries against 'Expert DNA' vectors, ensuring deterministic AI outputs only for high-stakes industrial tasks with DeepSeek 70B on NVIDIA L4.",[97],"NF2_5_xq0uI9RAVnZKiamA6oV3F-WLp2Hq8aiFUOsgk",{"id":3744,"title":3745,"ai":3746,"body":3751,"categories":3779,"created_at":73,"date_modified":73,"description":3780,"extension":74,"faq":73,"featured":75,"kicker_label":73,"meta":3781,"navigation":83,"path":3782,"published_at":3783,"question":73,"scraped_at":3784,"seo":3785,"sitemap":3786,"source_id":3787,"source_name":3788,"source_type":3789,"source_url":3790,"stem":3791,"tags":3792,"thumbnail_url":73,"tldr":3793,"tweet":73,"unknown_tags":3794,"__hash__":3795},"summaries\u002Fsummaries\u002Fc17309e56b899c59-10x-claude-with-agents-memory-context-and-skills-m-summary.md","10x Claude with Agents, Memory, Context, and Skills MD Files",{"provider":7,"model":8,"input_tokens":3747,"output_tokens":3748,"processing_time_ms":3749,"cost_usd":3750},3344,1411,14122,0.0013518,{"type":14,"value":3752,"toc":3774},[3753,3757,3760,3764,3767,3771],[17,3754,3756],{"id":3755},"personalize-claudes-behavior-from-the-start","Personalize Claude's Behavior from the Start",[22,3758,3759],{},"Start by creating agents.md as your AI's onboarding document. Include your business details, preferred voice, and work style—Claude references this file before every interaction, ensuring outputs align with your needs consistently. Pair it with memory.md to log and update user preferences dynamically, like instructing 'stop signing emails with cheers.' This continuous learning prevents repetitive fixes and builds a tailored assistant that adapts over time, maximizing the value of a Claude subscription beyond basic queries.",[17,3761,3763],{"id":3762},"load-deep-context-without-overloading-prompts","Load Deep Context Without Overloading Prompts",[22,3765,3766],{},"Use a context folder for heavier, nuanced information that standalone prompts can't handle effectively. Claude pulls from here as needed, combining it with preferences from memory.md. This setup avoids context bloat in individual chats while providing rich background, enabling more accurate and relevant responses for complex tasks.",[17,3768,3770],{"id":3769},"turn-processes-into-one-shot-workflows","Turn Processes into One-Shot Workflows",[22,3772,3773],{},"In the skills folder, demonstrate a process once—Claude packages it into a reusable workflow. This compresses multi-step, time-intensive work, like 4-hour tasks, into a single instruction. The result: scalable automation where you define expertise upfront, then invoke it repeatedly without reteaching, effectively 10x-ing productivity on repetitive engineering or product workflows.",{"title":65,"searchDepth":66,"depth":66,"links":3775},[3776,3777,3778],{"id":3755,"depth":66,"text":3756},{"id":3762,"depth":66,"text":3763},{"id":3769,"depth":66,"text":3770},[110],"I sit down with Remy Gaskell to break down how anyone can build AI agents to run entire departments of their business. Remy walks through the core concepts: agent loops, context files, memory, MCP tool connections, and skills. We put everything together by building a fully functional executive assistant live on screen. This is a beginner-friendly crash course that covers Claude Code, Codex, Cowork, Antigravity, Manus, and OpenClaw, showing that once you understand how to \"drive,\" you can jump into any agent platform. By the end, listeners know exactly how to set up markdown-based context files, connect their everyday tools, and create reusable skills that compound over weeks and months.\n\nKey Points\n\n* Agent platforms (Claude Code, Codex, Cowork, Antigravity, Manus, OpenClaw) are all running the same observe-think-act loop under the hood — learning one means you can use any of them.\n* The shift from chat to agents requires moving from prompt engineering to context engineering: load the agent with rich context so simple prompts produce excellent results.\n* A memory md file creates a self-improving loop where the agent learns preferences across sessions and makes fewer errors over time.\n* MCP (Model Context Protocol), built by Anthropic, acts as a universal translator between your agent and every tool it needs — Gmail, Calendar, Stripe, Notion, and more.\n* Skills are reusable SOPs packaged as markdown files; once you explain a process once, you can invoke it repeatedly, and they compound as you add three to five per week.\n* Scheduled tasks turn skills into automated workflows — morning briefs, car searches, ad library analyses — that run on a cron without any manual trigger.\n\nNumbered Section Summaries\n\n1. The Agent Loop in Action\n\nRemy kicks off with a live demo, sending the same prompt — \"build a minimalist portfolio site for Greg Isenberg\" — to Claude Code, Codex, and Antigravity simultaneously. All three platforms run the same observe-think-act loop: research the subject, write the code, spin up a preview, and verify the result with a screenshot. The demo makes it tangible that every agent harness is just a different car with the same engine.\n\n2. Onboarding Your Agent Like a Real Employee\n\nRemy shows that without context, an agent asked to \"write me a cold email\" has no idea who you are or what you sell. The fix is an agents.md (or Claude.md) file — a persistent context document loaded at the start of every session. You fill it with your role, business details, tools, and working preferences, and the result is that a two-word prompt produces a fully informed output.\n\n3. Memory That Compounds\n\nChat models store memory invisibly in the cloud; agents require you to build it intentionally. Remy adds a memory.md file and a simple instruction in the context file: \"When I correct you or you learn something new, update memory.md.\" Preferences like tone, email sign-offs, and design choices persist across sessions, and errors decrease over time.\n\n\nThe #1 tool to find startup ideas\u002Ftrends - https:\u002F\u002Fwww.ideabrowser.com\u002F\n\nLCA helps Fortune 500s and fast-growing startups build their future - from Warner Music to Fortnite to Dropbox. We turn 'what if' into reality with AI, apps, and next-gen products https:\u002F\u002Flatecheckout.agency\u002F\n\nThe Vibe Marketer - Resources for people into vibe marketing\u002Fmarketing with AI: https:\u002F\u002Fwww.thevibemarketer.com\u002F\n\nFIND ME ON SOCIAL\nX\u002FTwitter: https:\u002F\u002Ftwitter.com\u002Fgregisenberg\nInstagram: https:\u002F\u002Finstagram.com\u002Fgregisenberg\u002F\nLinkedIn: https:\u002F\u002Fwww.linkedin.com\u002Fin\u002Fgisenberg\u002F\n\nFIND REMY ON SOCIAL\nX:https:\u002F\u002Fx.com\u002Fremy_gaskell\nYoutube: https:\u002F\u002Fwww.youtube.com\u002F@aiwithremy\nInstagram: https:\u002F\u002Fwww.instagram.com\u002Faiwithremy\u002F",{},"\u002Fsummaries\u002Fc17309e56b899c59-10x-claude-with-agents-memory-context-and-skills-m-summary","2026-03-31 16:50:31","2026-04-03 21:16:06",{"title":3745,"description":3780},{"loc":3782},"c17309e56b899c59","Greg Isenberg","video","https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=ovLAIhbk3ek","summaries\u002Fc17309e56b899c59-10x-claude-with-agents-memory-context-and-skills-m-summary",[95,96,97],"Create four .md files—agents.md for business onboarding, memory.md for evolving preferences, context folder for nuanced info, and skills folder for reusable workflows—to turn 4-hour tasks into single-prompt executions.",[97],"u2cnAlNGnLm0YWszObIoUJdI3XkmB6GifoApTGwBXbo",{"id":3797,"title":3798,"ai":3799,"body":3804,"categories":3841,"created_at":73,"date_modified":73,"description":65,"extension":74,"faq":73,"featured":75,"kicker_label":73,"meta":3842,"navigation":83,"path":3854,"published_at":3855,"question":73,"scraped_at":3856,"seo":3857,"sitemap":3858,"source_id":3859,"source_name":3860,"source_type":3789,"source_url":3861,"stem":3862,"tags":3863,"thumbnail_url":3865,"tldr":3866,"tweet":3867,"unknown_tags":3868,"__hash__":3869},"summaries\u002Fsummaries\u002Fb24309283167b83a-malleable-evals-adaptive-testing-for-changing-ai-a-summary.md","Malleable Evals: Adaptive Testing for Changing AI Agents",{"provider":7,"model":8,"input_tokens":3800,"output_tokens":3801,"processing_time_ms":3802,"cost_usd":3803},6788,1435,19735,0.0020526,{"type":14,"value":3805,"toc":3836},[3806,3810,3813,3816,3820,3823,3826,3830,3833],[17,3807,3809],{"id":3808},"static-benchmarks-fail-malleable-ai-systems","Static Benchmarks Fail Malleable AI Systems",[22,3811,3812],{},"Traditional software evals rely on unit tests, regression suites, CI\u002FCD, and chaos engineering to measure static code. AI agents break this: they adapt to users, rewrite harnesses like OpenClaw (where Vincent Koc is a core contributor), and exhibit behavioral drift over time. Handcrafted datasets miss 20% edge cases that break products, test suites stale quickly, and production traces reveal issues benchmarks ignore. Result: hyperfocus on static benchmarks at conferences, yet systems ship with unmeasured chaos. Trade-off: offline evals ensure compliance (e.g., no illegal financial advice) but skip real-world stretching, leaving gaps until failures hit.",[22,3814,3815],{},"Chaos engineering—randomly breaking systems to find limits—applies here but lacks in AI. Software now malleables too, shipping at lightning speed; benchmarks can't keep up without adapting.",[17,3817,3819],{"id":3818},"shift-from-prompt-to-intent-engineering-compounds-eval-challenges","Shift from Prompt to Intent Engineering Compounds Eval Challenges",[22,3821,3822],{},"AI evolved: prompt engineering (random word-bashing for outputs, like accidental painkillers from liver meds) died by 2023. Context engineering added RAG, tools, search—enabling modular testing of agent parts (e.g., sales MCP tools). Now, 2025's intent engineering: cheap, fast tokens fuel self-optimizing agents understanding user intent via harnesses like OpenClaw, Claude, or CodeEx. Models solve human-hard ARC-AGI 2\u002F3 puzzles via pattern recognition.",[22,3824,3825],{},"Problem: personalized experiences vary by user, making evals harder. Agents seem \"insecure\" without insight into layers. Need: measure ambiguity, personality rubrics (like art grading), not just 1+1=2.",[17,3827,3829],{"id":3828},"build-living-evals-as-self-optimizing-agents","Build Living Evals as Self-Optimizing Agents",[22,3831,3832],{},"Define end-state intent (e.g., user reward signal), let agents curate suites from traces: 80% traces repeat, but customer shifts trigger changes—agents detect, alert owners, update tests. Run online, always-on optimization; integrate telemetry (errors, costs) for self-correction—heal issues without prediction.",[22,3834,3835],{},"Applies broadly: auto-optimization like Python reward loops tunes anything (e.g., BBQ mixes). Evals become code\u002Fliving agents, not datasets: 80% static intent-defined, 20% adaptive for weird queries. At Comet, they're implementing; mindset: treat evals agentically as problem\u002Fdata shift.",{"title":65,"searchDepth":66,"depth":66,"links":3837},[3838,3839,3840],{"id":3808,"depth":66,"text":3809},{"id":3818,"depth":66,"text":3819},{"id":3828,"depth":66,"text":3829},[],{"content_references":3843,"triage":3851},[3844,3846,3849],{"type":3718,"title":3845,"context":3721},"OpenClaw",{"type":3847,"title":3848,"context":3721},"dataset","ARC-AGI 2",{"type":3723,"title":3850,"context":3721},"Adaptive testing for LLM evals paper",{"relevance":79,"novelty":80,"quality":80,"actionability":80,"composite":3852,"reasoning":3853},4.35,"Category: AI & LLMs. The article discusses the need for adaptive evaluation methods for AI agents, addressing a specific pain point about traditional static benchmarks failing to measure dynamic AI behavior. It provides actionable insights on building self-optimizing evaluation suites that can adapt to user intent, which is directly applicable to product builders working with AI.","\u002Fsummaries\u002Fb24309283167b83a-malleable-evals-adaptive-testing-for-changing-ai-a-summary","2026-05-12 16:00:06","2026-05-13 12:00:22",{"title":3798,"description":65},{"loc":3854},"b24309283167b83a","AI Engineer","https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=4VhbYlfC7Gs","summaries\u002Fb24309283167b83a-malleable-evals-adaptive-testing-for-changing-ai-a-summary",[3864,95,96,97],"agents","https:\u002F\u002Fi.ytimg.com\u002Fvi\u002F4VhbYlfC7Gs\u002Fhqdefault.jpg","Static benchmarks fail self-adapting agents; use production traces for agent-curated, always-on eval suites that self-optimize toward user intent.","[Vincent Koc](https:\u002F\u002Fx.com\u002Fvincent_koc)'s conference talk on why static benchmarks fail for adaptive AI agents like OpenClaw, pushing a shift to \"malleable evals\" where agents self-generate test suites from production traces to handle behavioral drift and edge cases.",[97],"1xd66DttG0MMlsYN5aUG3JKuQffY2t3Ws867t1cXQiI",{"id":3871,"title":3872,"ai":3873,"body":3878,"categories":4397,"created_at":73,"date_modified":73,"description":65,"extension":74,"faq":73,"featured":75,"kicker_label":73,"meta":4398,"navigation":83,"path":4409,"published_at":4410,"question":73,"scraped_at":4411,"seo":4412,"sitemap":4413,"source_id":4414,"source_name":3860,"source_type":3789,"source_url":4415,"stem":4416,"tags":4417,"thumbnail_url":4418,"tldr":4419,"tweet":4420,"unknown_tags":4421,"__hash__":4422},"summaries\u002Fsummaries\u002F05800069e15ecf07-agentic-search-powers-80-of-llm-context-engineerin-summary.md","Agentic Search Powers 80% of LLM Context Engineering",{"provider":7,"model":8,"input_tokens":3874,"output_tokens":3875,"processing_time_ms":3876,"cost_usd":3877},8105,2819,34902,0.00273965,{"type":14,"value":3879,"toc":4389},[3880,3884,3896,3911,3919,3923,3926,3949,3958,3961,4023,4026,4029,4033,4036,4065,4068,4071,4074,4078,4085,4102,4312,4315,4318,4322,4340,4346,4349,4353,4385],[17,3881,3883],{"id":3882},"context-engineering-demystified-agentic-search-at-the-core","Context Engineering Demystified: Agentic Search at the Core",[22,3885,3886,3887,3891,3892,3895],{},"Context engineering selects what enters an LLM's context window from diverse sources like local files, databases, web, working memory, agent skills, and long-term memory. Leonie argues it's 80% agentic search—the mechanisms deciding ",[3888,3889,3890],"em",{},"what"," and ",[3888,3893,3894],{},"how"," to retrieve—over model choice. Early RAG used fixed vector search on user queries, retrieving irrelevant chunks or missing multi-hop needs. Agentic RAG introduces tools letting agents decide: retrieve? Rewrite query? Multi-round? This evolves retrieval from rigid pipelines to dynamic decisions.",[22,3897,3898,3899,3903,3904,3903,3907,3910],{},"Key principle: No single tool suffices. Native tools handle sources (e.g., file search for codebases, SQL\u002FESQL for DBs, web scrapers), but shell tools (LangChain's shell, Anthropic's bash, OpenAI's exec) add versatility via CLI commands like ",[3900,3901,3902],"code",{},"ls",", ",[3900,3905,3906],{},"grep",[3900,3908,3909],{},"curl",". Combine them: Vector for semantics, keyword for exact matches, general-purpose for complex filters. Trade-off: Shell is flexible but risky (security, errors); specialized tools are reliable but narrow.",[22,3912,3913,3914,3918],{},"\"Context engineering is about 80% agentic search because it's this little box right here ",[3915,3916,3917],"span",{},"arrow from sources to window",".\"",[17,3920,3922],{"id":3921},"building-reliable-search-tools-descriptions-and-parameters","Building Reliable Search Tools: Descriptions and Parameters",[22,3924,3925],{},"Effective tools start with precise descriptions. Poor ones: One-sentence generics (\"Search the database\"). Good ones specify:",[3927,3928,3929,3937,3943],"ul",{},[3930,3931,3932,3936],"li",{},[3933,3934,3935],"strong",{},"Core purpose",": What it does.",[3930,3938,3939,3942],{},[3933,3940,3941],{},"Triggers",": When to use (e.g., \"For conference sessions on AI constraints\"), avoid (e.g., \"Not for web data\").",[3930,3944,3945,3948],{},[3933,3946,3947],{},"Relationships",": Sequence (e.g., \"Load ESQL skill first\").",[22,3950,3951,3952,3954,3955,3918],{},"Reinforce in system prompts: \"You are a search agent... decide if retrieval needed. Use ",[3915,3953,3718],{}," for ",[3915,3956,3957],{},"condition",[22,3959,3960],{},"Parameter complexity scales failure risk:",[3962,3963,3964,3980],"table",{},[3965,3966,3967],"thead",{},[3968,3969,3970,3974,3977],"tr",{},[3971,3972,3973],"th",{},"Complexity",[3971,3975,3976],{},"Example",[3971,3978,3979],{},"Agent Challenge",[3981,3982,3983,3997,4010],"tbody",{},[3968,3984,3985,3989,3994],{},[3986,3987,3988],"td",{},"Low",[3986,3990,3991],{},[3900,3992,3993],{},"get_customer(id: str)",[3986,3995,3996],{},"Easy ID generation.",[3968,3998,3999,4002,4007],{},[3986,4000,4001],{},"Medium",[3986,4003,4004],{},[3900,4005,4006],{},"semantic_search(query: str, k: int=3, filters: dict)",[3986,4008,4009],{},"Balancing params.",[3968,4011,4012,4015,4020],{},[3986,4013,4014],{},"High",[3986,4016,4017],{},[3900,4018,4019],{},"execute_esql(query: str)",[3986,4021,4022],{},"Full query syntax.",[22,4024,4025],{},"Always add try-except for self-correction: Return errors to agent (e.g., invalid wildcard) instead of crashing. Test tools standalone before agent integration.",[22,4027,4028],{},"\"Tool description is the most important aspect... add trigger conditions, relationships.\"",[17,4030,4032],{"id":4031},"diagnosing-and-fixing-agent-failure-modes","Diagnosing and Fixing Agent Failure Modes",[22,4034,4035],{},"Agents fail in predictable ways—address systematically:",[4037,4038,4039,4045,4051],"ol",{},[3930,4040,4041,4044],{},[3933,4042,4043],{},"No tool called",": Relies on parametric knowledge. Fix: Prompt \"Always retrieve for factual queries.\"",[3930,4046,4047,4050],{},[3933,4048,4049],{},"Wrong tool",": Picks web over DB. Fix: Detailed descriptions + system prompt prioritization.",[3930,4052,4053,4056,4057,4060,4061,4064],{},[3933,4054,4055],{},"Wrong parameters",": E.g., SQL ",[3900,4058,4059],{},"%"," wildcard vs. ",[3900,4062,4063],{},"*"," in ESQL. Fix: Skills for docs.",[22,4066,4067],{},"Quality criteria: Tool returns relevant, non-zero results (zero may signal rewrite). Evaluate: Does output cite retrieved context? Multi-turn coherence?",[22,4069,4070],{},"Common mistake: Over-relying on semantics—fails keywords (\"GPA\" matches \"Gemma\" via tokens). Solution: Hybrid stacks.",[22,4072,4073],{},"\"The most challenging aspect... was getting the agent to not call the web search tool but the database search tool.\"",[17,4075,4077],{"id":4076},"step-by-step-semantic-to-general-purpose-retrieval-with-skills","Step-by-Step: Semantic to General-Purpose Retrieval with Skills",[22,4079,4080,4081,4084],{},"Assumes: Python\u002FLangChain basics, local ElasticSearch cluster, chunked\u002Findexed data (e.g., conference sessions: ",[3900,4082,4083],{},"text"," embedded, metadata filterable).",[22,4086,4087,4090,4091,3903,4094,4097,4098,4101],{},[3933,4088,4089],{},"Prerequisites",": Mid-level (built basic agents); install ",[3900,4092,4093],{},"langchain",[3900,4095,4096],{},"elasticsearch",", embedding model (e.g., ",[3900,4099,4100],{},"gte-large-en-v1.5",").",[4037,4103,4104,4180,4248],{},[3930,4105,4106,4109,4110,4169,4172,4173,4175,4176,4179],{},[3933,4107,4108],{},"Vanilla Semantic Tool"," (Brittle baseline):",[4111,4112,4116],"pre",{"className":4113,"code":4114,"language":4115,"meta":65,"style":65},"language-python shiki shiki-themes github-light github-dark","from langchain_community.vectorstores import ElasticVectorSearch\nfrom langchain.tools import tool\nembeddings = HuggingFaceEmbeddings(model_name=\"thenlper\u002Fgte-large\")\nvectorstore = ElasticVectorSearch(elasticsearch_url, index_name, embeddings)\n@tool\ndef semantic_search(query: str) -> str:\n    \"\"\"Search conference sessions semantically.\"\"\"\n    docs = vectorstore.similarity_search(query, k=3)\n    return \"\\n\".join([doc.page_content for doc in docs])\n","python",[3900,4117,4118,4125,4130,4135,4140,4145,4151,4157,4163],{"__ignoreMap":65},[3915,4119,4122],{"class":4120,"line":4121},"line",1,[3915,4123,4124],{},"from langchain_community.vectorstores import ElasticVectorSearch\n",[3915,4126,4127],{"class":4120,"line":66},[3915,4128,4129],{},"from langchain.tools import tool\n",[3915,4131,4132],{"class":4120,"line":3727},[3915,4133,4134],{},"embeddings = HuggingFaceEmbeddings(model_name=\"thenlper\u002Fgte-large\")\n",[3915,4136,4137],{"class":4120,"line":80},[3915,4138,4139],{},"vectorstore = ElasticVectorSearch(elasticsearch_url, index_name, embeddings)\n",[3915,4141,4142],{"class":4120,"line":79},[3915,4143,4144],{},"@tool\n",[3915,4146,4148],{"class":4120,"line":4147},6,[3915,4149,4150],{},"def semantic_search(query: str) -> str:\n",[3915,4152,4154],{"class":4120,"line":4153},7,[3915,4155,4156],{},"    \"\"\"Search conference sessions semantically.\"\"\"\n",[3915,4158,4160],{"class":4120,"line":4159},8,[3915,4161,4162],{},"    docs = vectorstore.similarity_search(query, k=3)\n",[3915,4164,4166],{"class":4120,"line":4165},9,[3915,4167,4168],{},"    return \"\\n\".join([doc.page_content for doc in docs])\n",[4170,4171],"br",{},"Works: \"Regulatory constraints\" → relevant talks.\nFails: \"GPA\" → Gemma models (semantic drift).",[4170,4174],{},"Agent: ",[3900,4177,4178],{},"create_react_agent(llm, [semantic_search], system_prompt)",".",[3930,4181,4182,4185,4186,4241,4243,4244,4247],{},[3933,4183,4184],{},"General-Purpose ESQL Tool"," (More flexible, error-prone):\nSwitch to GPT-4o-mini (nano too weak for query gen).",[4111,4187,4189],{"className":4113,"code":4188,"language":4115,"meta":65,"style":65},"from elasticsearch import Elasticsearch\nclient = Elasticsearch(\"http:\u002F\u002Flocalhost:9200\")\n@tool\ndef execute_esql_query(esql_query: str) -> str:\n    \"\"\"Execute ESQL against conference index. Use ESQL skill first.\"\"\"\n    try:\n        response = client.esql.query(esql={\"query\": esql_query})\n        return json.dumps(response)\n    except Exception as e:\n        return f\"Error: {str(e)}\"\n",[3900,4190,4191,4196,4201,4205,4210,4215,4220,4225,4230,4235],{"__ignoreMap":65},[3915,4192,4193],{"class":4120,"line":4121},[3915,4194,4195],{},"from elasticsearch import Elasticsearch\n",[3915,4197,4198],{"class":4120,"line":66},[3915,4199,4200],{},"client = Elasticsearch(\"http:\u002F\u002Flocalhost:9200\")\n",[3915,4202,4203],{"class":4120,"line":3727},[3915,4204,4144],{},[3915,4206,4207],{"class":4120,"line":80},[3915,4208,4209],{},"def execute_esql_query(esql_query: str) -> str:\n",[3915,4211,4212],{"class":4120,"line":79},[3915,4213,4214],{},"    \"\"\"Execute ESQL against conference index. Use ESQL skill first.\"\"\"\n",[3915,4216,4217],{"class":4120,"line":4147},[3915,4218,4219],{},"    try:\n",[3915,4221,4222],{"class":4120,"line":4153},[3915,4223,4224],{},"        response = client.esql.query(esql={\"query\": esql_query})\n",[3915,4226,4227],{"class":4120,"line":4159},[3915,4228,4229],{},"        return json.dumps(response)\n",[3915,4231,4232],{"class":4120,"line":4165},[3915,4233,4234],{},"    except Exception as e:\n",[3915,4236,4238],{"class":4120,"line":4237},10,[3915,4239,4240],{},"        return f\"Error: {str(e)}\"\n",[4170,4242],{},"Agent generates: ",[3900,4245,4246],{},"from conference_sessions where text like '%GPA%'"," → Error (wrong wildcard). Self-corrects next turn.",[3930,4249,4250,4253,4254],{},[3933,4251,4252],{},"Agent Skills for Progressive Disclosure",":",[3927,4255,4256,4298,4305],{},[3930,4257,4258,4259,4262],{},"Markdown file: ",[3900,4260,4261],{},"skills\u002Fesql.md",[4111,4263,4267],{"className":4264,"code":4265,"language":4266,"meta":65,"style":65},"language-markdown shiki shiki-themes github-light github-dark","---\nname: ESQL Skill\ndescription: Generate ESQL queries.\n---\nStructure: from index | where condition | limit k\nStrings: double quotes. Wildcards: * not %.\n","markdown",[3900,4268,4269,4274,4279,4284,4288,4293],{"__ignoreMap":65},[3915,4270,4271],{"class":4120,"line":4121},[3915,4272,4273],{},"---\n",[3915,4275,4276],{"class":4120,"line":66},[3915,4277,4278],{},"name: ESQL Skill\n",[3915,4280,4281],{"class":4120,"line":3727},[3915,4282,4283],{},"description: Generate ESQL queries.\n",[3915,4285,4286],{"class":4120,"line":80},[3915,4287,4273],{},[3915,4289,4290],{"class":4120,"line":79},[3915,4291,4292],{},"Structure: from index | where condition | limit k\n",[3915,4294,4295],{"class":4120,"line":4147},[3915,4296,4297],{},"Strings: double quotes. Wildcards: * not %.\n",[3930,4299,4300,4301,4304],{},"Tools: ",[3900,4302,4303],{},"create_openai_functions_agent"," + skill loader middleware.",[3930,4306,4307,4308,4311],{},"Updated prompt\u002Ftool: \"Load ESQL skill before execute_esql_query.\"\nResult: Agent loads skill → ",[3900,4309,4310],{},"from conference_sessions where text like '*GPA*' | limit 3"," → Exact match (Samuel's talk).",[22,4313,4314],{},"Fits in workflow: Post-indexing, pre-agent loop. Practice: Index your data, break semantic tool, iterate fixes.",[22,4316,4317],{},"\"Doing good search is incredibly difficult... curate your own stack.\"",[17,4319,4321],{"id":4320},"shell-tools-filesystem-and-beyond","Shell Tools: Filesystem and Beyond",[22,4323,4324,4325,3903,4327,3903,4329,4332,4333,4336,4337,4179],{},"Shell unlocks local files: ",[3900,4326,3902],{},[3900,4328,3906],{},[3900,4330,4331],{},"cat",". LangChain ",[3900,4334,4335],{},"@tool"," wraps ",[3900,4338,4339],{},"subprocess",[22,4341,4342,4343,4345],{},"Limitations: No built-in semantics; security (sandbox). Extend: Custom CLIs (e.g., DB CLI, ",[3900,4344,3909],{}," for web).",[22,4347,4348],{},"Teaser: File search beats naive recursion for code agents; combine with skills for CLI docs.",[17,4350,4352],{"id":4351},"key-takeaways","Key Takeaways",[3927,4354,4355,4358,4361,4364,4367,4370,4373,4376,4379,4382],{},[3930,4356,4357],{},"Prioritize agentic search: 80% of context engineering success.",[3930,4359,4360],{},"Craft tool descriptions with purpose, triggers, relationships; reinforce in prompts.",[3930,4362,4363],{},"Add try-except everywhere—let agents self-correct from errors.",[3930,4365,4366],{},"Use skills for complex params (e.g., query langs); progressive disclosure scales docs.",[3930,4368,4369],{},"Hybrid tools: Semantic for concepts, keyword\u002FESQL for exact, shell for files\u002Fweb.",[3930,4371,4372],{},"Test standalone: Break with keywords\u002Ffilters before agent.",[3930,4374,4375],{},"Model matters: Nano for simple, mini+ for query gen.",[3930,4377,4378],{},"Zero results? Rewrite, don't answer.",[3930,4380,4381],{},"Sequence tools: Skills → General query → Shell fallback.",[3930,4383,4384],{},"Build stacks, not silos: Match source to tool.",[4386,4387,4388],"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":65,"searchDepth":66,"depth":66,"links":4390},[4391,4392,4393,4394,4395,4396],{"id":3882,"depth":66,"text":3883},{"id":3921,"depth":66,"text":3922},{"id":4031,"depth":66,"text":4032},{"id":4076,"depth":66,"text":4077},{"id":4320,"depth":66,"text":4321},{"id":4351,"depth":66,"text":4352},[110],{"content_references":4399,"triage":4407},[4400,4402,4405],{"type":3718,"title":4401,"context":3721},"LangChain",{"type":3718,"title":4403,"author":4404,"context":3721},"ElasticSearch","Elastic",{"type":3723,"title":4406,"author":4404,"context":3721},"ESQL",{"relevance":79,"novelty":80,"quality":80,"actionability":80,"composite":3852,"reasoning":4408},"Category: AI & LLMs. The article provides a deep dive into context engineering and agentic search, which are crucial for building AI-powered products. It offers practical insights on tool descriptions and retrieval strategies that can be directly applied by developers and product builders.","\u002Fsummaries\u002F05800069e15ecf07-agentic-search-powers-80-of-llm-context-engineerin-summary","2026-05-08 13:00:06","2026-05-10 15:05:55",{"title":3872,"description":65},{"loc":4409},"de920282ce217f10","https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=ynJyIKwjonM","summaries\u002F05800069e15ecf07-agentic-search-powers-80-of-llm-context-engineerin-summary",[3864,95,96,97],"https:\u002F\u002Fi.ytimg.com\u002Fvi\u002FynJyIKwjonM\u002Fhqdefault.jpg","Context engineering relies on agentic search tools to pull relevant data from files, DBs, web, and memory. Master tool descriptions, skills, and shell tools to avoid brittle retrieval—demoed with ElasticSearch and LangChain.","Practical workshop by Elastic's Leonie Monigatti on building reliable agentic search stacks for LLM context engineering—demos semantic search, database queries (e.g., ESQL\u002FSQL), shell tools, and hybrids, plus tool description tips and failure mode fixes with live code.",[97],"petQDU6OhxttglviH_WlFwK-aisF7C8n9iqKN9uDLhw"]