Every insight and case study, grouped by the problem it addresses (retrieval, cost, evals, production, and architecture) instead of scattered by publish date.
Retrieval-augmented generation looks simple in a demo and breaks on real corpora — messy PDFs, thin chunking, and stale embeddings all masquerade as model problems. This is our work on making retrieval actually hold up in production.
Browse →Inference cost rarely matches the per-token sticker price once retries, context growth, and fallback calls are counted. This is our work on modeling real AI spend and the build-vs-buy calls that keep it in check.
Browse →Shipping an AI feature without an eval suite means every prompt or model change is a guess about whether quality went up or down. This is our work on evals, CI, and the observability that catches regressions after ship.
Browse →The gap between a working notebook and a system that runs reliably, observably, and cheaply at scale is usually a bigger lift than the original prototype. This is our work on closing that gap.
Browse →Build, buy, or skip; lock into one model provider or stay agnostic — these architectural calls compound over the life of a system. This is our work on making them deliberately instead of by default.
Browse →Getting an AI feature past InfoSec, privacy, risk, and procurement is where most mid-market and enterprise projects actually stall, not in the model. This is our work on the threat models, guardrails, and evidence that get a review past 'no.'
Browse →