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
#llmops
7 posts
Tech Opinions · 2 min
Making AI production-ready isn't about the model
Shipping production AI isn't about model benchmarks — it's the reliability, retries, fallbacks, observability, and cost discipline that keep LLM systems alive.
AI & Engineering · 7 min
Agentic Workflows That Don't Spiral Out of Control
Reliable agents come from control, not capability. Cap turns and time, push predictable steps into a state machine, and keep a human on the irreversible ones.
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.
AI & Engineering · 6 min
RAG vs Fine-Tuning vs Long Context: My Decision Framework
'Should we fine-tune?' is usually the wrong first question. Ask instead: is the gap knowledge or behavior, and how often does the answer change?