LLM evaluation engineer at a Copenhagen AI safety company, designing benchmarks for alignment and factuality testing. Built the automated red-teaming framework that runs 10 K adversarial prompts per model release.
Learn how to build robust, red-teamed legal extraction pipelines using structured output, validation schemas, and automated adversarial testing.
7 August 2026
Retrieval-augmented generation transforms a generic LLM into a domain expert by grounding its answers in your own data. This guide walks through building a production-ready RAG pipeline using LangChain, PostgreSQL with pgvector, and the Anthropic Claude API — covering chunking strategies, embedding storage, retrieval tuning, hybrid search, evaluation, and the operational concerns that separate a demo from a deployed system.
16 July 2026