15 posts found
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.
Every time Claude goes down, someone makes a joke on X. I think the joke is hiding a serious architecture decision: is it worth depending on an intelligence that occasionally leaves you stranded?
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.
#1 on MTEB English by 5.09 points. 5 modalities in a single 3,072-dimensional vector. Matryoshka to truncate to 768 without retraining. Text, images, 128s video, 80s audio, 6-page PDFs. Google solved the problem that forced teams to maintain 3 models and 3 indexes.
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.
GPT-4o: from 91% with 5 words to 15% with 40. In mixed conditions: 1%. GPT-5, Claude Opus 4.1 and Gemini 2.5 also failed. A PNAS Nexus paper proved: transformers have no 'referee' for resolving attention conflicts. It's an architectural limitation.
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.
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.
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?
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.
Did Claude Skills come to replace MCP? No. They're complementary layers of the same architecture. Here's why using both together is the right answer.