AI & Engineering · 6 min
Hallucination is not a model problem — it's a system design problem
Ground the model in retrieved evidence, constrain its output, verify its claims, and measure everything. A layered defense against LLM hallucination.
Tag
#evaluation
5 posts
AI & Engineering · 7 min
How We Actually Measure RAG Quality
RAG quality by vibes doesn't survive a second engineer. Decompose by stage, build the eval set from real failures, calibrate the LLM judge, and gate CI.
AI & Engineering · 8 min
POC to Production: What Breaks When RAG Meets Real Users
A RAG demo proves the happy path exists. Production is everything else — tracing, drift, evals, and learning to say 'I don't have that part.'
AI & Engineering · 7 min
The Hallucination Problem: What Reduced Ours, What Didn't
Most LLM hallucination is a retrieval failure in disguise. Fix the context first, force citations, and give the model a sanctioned way to say 'I don't know.'
AI & Engineering · 7 min
Cutting LLM Inference Cost ~40% Without Losing Quality
Inference cost optimization is a measurement problem in disguise. Fix the quality metric first, then trim context, route models, and cache the stable prefix.