Topic
LLMs
9 essays
Designing LLM Features Around Structured Outputs You Can Trust
Treat the schema as the product contract. Here is how I design typed LLM outputs, validate and repair them, version the shape, and eval field by field.
Context Windows Are Not Memory
A long context window feels like memory, but it is just a working set. Real memory needs selection, persistence, and a plan for what to forget.
RAG Evaluations That Actually Catch Regressions
A practical framework for building retrieval and answer-quality evaluations that catch real regressions instead of producing vanity metrics.
Prompt Caching Is a Product Feature, Not Just an Infra Trick
Why prompt caching changes latency, cost, and product design decisions — and why teams should treat it as a user-facing capability.
AI Search Is Changing SEO Into Something More Interesting
Why AI search changes how brands earn visibility, and why the next era of search is less about ranking pages and more about becoming a trusted source.
How I Think About Model Routing in Production
A practical way to think about model routing across cheap, fast, and strong models without turning your stack into a chaotic science project.
When Not to Use RAG
RAG is powerful, but it is not a default answer to every AI product problem. Here are the cases where I would avoid it or simplify it.
Synthetic Data for LLM Evals: Useful, but Easy to Abuse
Synthetic data can speed up AI evaluation pipelines, but only if it is used with strong guardrails and real-world calibration.
AI Browsers Are Turning Search Into a Workflow
Why AI browsers matter: they collapse reading, searching, summarizing, and acting into a single interface.