How we replaced fragile prompt chains with typed schemas, validation gates, and evaluation at every step — 94% task success vs 60% baseline
January 2024. We built a "research agent" — 12 prompts chained together:
How we moved from "semantic search + hope" to a measured, tunable retrieval pipeline with 95% recall@10
Everyone ships RAG the same way: chunk by 512 tokens, embed with text-embedding-3-small, top-k=5, stuff into context. It works for demos.
How we replaced "looks good to me" with automated evaluation catching 92% of hallucinations before deployment
Three months ago, our team shipped a RAG-based customer support assistant. It worked great in testing — we'd ask it questions, read the answers, and say "yeah, that looks right."
How we replaced fragile prompt chains with typed schemas, validation gates, and evaluation at every step — 94% task success vs 60% baseline
January 2024. We built a "research agent" — 12 prompts chained together:
How we moved from "semantic search + hope" to a measured, tunable retrieval pipeline with 95% recall@10
Everyone ships RAG the same way: chunk by 512 tokens, embed with text-embedding-3-small, top-k=5, stuff into context. It works for demos.
How we replaced "looks good to me" with automated evaluation catching 92% of hallucinations before deployment
Three months ago, our team shipped a RAG-based customer support assistant. It worked great in testing — we'd ask it questions, read the answers, and say "yeah, that looks right."