16 posts encontrados
Your RAG works great in testing and falls apart in production? I've been there. The problem is almost never the LLM — it's the retrieval pipeline. Here are the 4 steps I use to fix it for good.
Meta named the problem: Behavioral State Decay. The instruction is there. The AI 'sees' it. But stops acting on it. The fix: a second AI agent — the 'shoulder tap' — that injects selective reminders. +8.3 points on Terminal-Bench. Without retraining the model.
86.7% overlap between Claude's citations and Brave Search results. ReAct loop with up to 20 iterations. Dynamic filtering via Python code. Cost of $0.08-$0.25 per searched response. The anatomy of how an AI decides to verify facts instead of inventing them.
A compiled 3B model beat the same 3B model with external orchestration on 4 of 5 metrics (p<0.001). 128-462x cheaper. 2.8x faster. 87-98% quality. The paper challenging LangGraph, CrewAI, and the entire orchestration industry.
SkillOpt: 52 out of 52 wins. Zero losses. GPT-5.5 jumped from 58.8% to 82.3% average — without touching model weights. The secret? Treating a Markdown file as a 'trainable parameter.' Cost: 1 to 4 edits in a .md file.
Cursor 3 launched multi-agent with 8 parallel agents. Claude Code fits 30,000 lines in a single prompt. Diffs fail 40% of the time — full rewrites drop it to 0%. Here's the architecture that makes it all work.
Gartner predicts 40%+ of agentic AI projects will be scrapped by 2027. Anthropic says: 'don't build agents when workflows suffice.' And in 2026, the market conversation shifted from 'building agents' to 'orchestrating agents.' Start with the rails.
Prompt injection isn't a bug — it's a fundamental property of current LLM architecture. The Countermind paper proposes 4-layer defense. OWASP's Top 10 for LLMs lists prompt injection as vulnerability #1. And your 'hardened' system prompt won't save you.
FloTorch's February 2026 benchmark shocked the community: recursive 512-token chunking BEAT semantic. 80% of RAG failures trace to ingestion, not the LLM. And the 'simple' strategy is often the best. Here's the guide I wish I'd read sooner.
+8.3% accuracy. Up to 2.4x faster. 75.6% fewer tokens. Training cost: $4.27. RecursiveMAS proves that when AIs stop 'talking' and start 'thinking together,' everything changes.
Chroma tested 18 frontier models: ALL degrade with long context. Effective context can drop up to 99% below advertised. But prompt caching saves 41-80%. This is the guide I wish I'd read before burning $2,000 in tokens.
60% of production projects use both. The combined approach hits 96% accuracy vs 89% RAG-only and 91% fine-tuning-only. Salesforce's AI VP cut hallucinations from 12% to 3%. But where do you start?
85% per-step accuracy sounds good. But in a 10-step workflow, that's 20% success. Gartner predicts 40%+ of agentic AI projects will be scrapped by 2027. The fix isn't a better model — it's engineering I wish I'd learned sooner.
512,000 lines of code leaked. 1,906 files. And the revelation that shocked the community: the AI itself is a while loop with one core function. The other 98% is traditional software engineering. Brilliant.
Stanford proved that changing only the harness — without touching the model — creates up to 6x performance swings. Meta-Harness ranked #1 on TerminalBench-2, beating all human solutions. The problem was never the model.
In 2024, AI hallucinations cost $67.4 billion globally. A lawyer was sanctioned for citing cases invented by ChatGPT. A 2025 mathematical proof confirmed: hallucinations CANNOT be eliminated. Why?