AI & ML

The moment you want to use an LLM inside a program rather than a chat window, you need its output as structured data – and “please respond in JSON” plus hope is how you get 3am parse errors. The reliable ways to get clean structured output.

The first LLM bill is a shock, and the instinct — switch to a cheaper model — is usually the wrong lever. Where the money actually goes: the asymmetry between input and output pricing, the context you resend every single turn, and the caching almost nobody turns on.

You cannot ship an LLM feature without measuring output quality, and hand-grading does not scale. Using an LLM as a judge works — if you know about position bias, self-preference, and the scores that quietly drift. A practical guide to doing it honestly.

The model is not hallucinating — it is answering faithfully from bad context. Diagnose retrieval failures in order: is the passage in the index at all, does it rank, and is the embedding model actually the problem?

vLLM serves open-weight LLMs at high throughput using PagedAttention and continuous batching. Learn GPU memory tuning, tensor parallelism, prefix caching, the latency-throughput trade-off, and when self-hosting actually beats an API.

A technical guide to building, securing, and deploying Model Context Protocol servers that connect large language models to your enterprise tools and data.

A production setup guide for LLM observability covering tracing, evaluations, cost attribution, and the trade-offs between Langfuse and Helicone.

Production patterns for Claude 4.7 with 1M token context: prompt caching, cost math, document analysis pipelines, and when to choose long context over RAG.

Production guide to fine-tuning LLMs with LoRA and QLoRA. Covers dataset curation, training configuration, evaluation metrics, and efficient deployment strategies.