Evaluating LLM Applications: Getting Past 'It Looks Good to Me'
Shipping LLM features on vibes works until a prompt tweak silently breaks ten other cases. Here is how to build evals that catch regressions before your users do.
The Augmented Dev Β· Archive
Dynamics 365, Power Platform, AI, system design, and web development β filter by theme or search across every post.
Newsletter
One email per post. No spam, no tracking pixels, unsubscribe anytime.
5 of 325 articles tagged llm
Shipping LLM features on vibes works until a prompt tweak silently breaks ten other cases. Here is how to build evals that catch regressions before your users do.
Asking a model to 'return JSON' and parsing the result is how you get 3 a.m. pages. Tool schemas, constrained decoding, and validation turn a probabilistic text generator into a reliable API.
Most RAG systems that retrieve bad context aren't failing at embeddings or reranking β they're failing at chunking. How you split documents quietly decides what your model can ever find.
Retrieval-augmented generation demos look great. Production RAG is a different engineering problem β chunking, hybrid retrieval, reranking, evaluation, and the failure modes nobody mentions at conferences.
LLM-powered pipelines break in the same ways old monoliths did β when you stack synchronous calls. Queues, events, and the patterns that make a six-step AI pipeline survive a partial outage.