ai
Retrieval-Augmented Generation is one of the most deployed LLM patterns in enterprise AI, and one of the most frequently broken in production. The demos work. The proof of concept looks promising. Then the system goes live and retrieval quality drops, answers become unreliable, and the team either spends months debugging
ai
Most teams adopting LLMs in production hit a ceiling at around 60 to 70 percent of the model's actual capability. They assume the gap is a model limitation. Usually it is a prompting limitation: three structural errors that compound on each other: undefined output format, absent system context,
jobs
newsletter
A lot of the coverage of Claude's latest generation focuses on benchmarks. Pass rates on MMLU, scores on HumanEval, how it ranks on some leaderboard that changes every two weeks. That is mostly noise for people who actually use these models to get work done. What changed for
newsletter
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