6 posts found
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?