Every guide, architecture, comparison, insight, case study, pattern, failure mode and tool, grouped by the problem it addresses (retrieval, cost, evals, production, strategy, and security) instead of scattered by kind.
Retrieval-augmented generationRetrieval-Augmented Generation (RAG) retrieves relevant passages at query time and feeds them into an LLM's context. looks simple in a demo and breaks on real corpora — messy PDFs, thin chunking, and stale embeddingsEmbeddings are numeric vectors that place similar content close together in vector space. all masquerade as model problems. This is our work on making retrieval actually hold up in production.
Browse →Inference costInference cost is what it costs to run a trained model on a request. rarely matches the per-token sticker price once retries and context growth are counted. Fallback calls push it further. 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 suiteAn eval suite is a versioned set of test cases that measures whether outputs are good. means every prompt or model change is a guess about whether quality went up or down. This is our work on evals and the observabilityObservability captures traces of every prompt, retrieval, tool call, and response for debugging. that catches regressions after ship. Both belong in CI.
Browse →The gap between a working notebook and a system that runs reliably 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 and procurement is where most mid-market and enterprise projects actually stall, not in the model. Privacy and risk review are the other two gates. This is our work on the threat models and evidence that get a review past 'no.'
Browse →Source: https://customlabs.io/topics/