RAG, evals, and document-to-knowledge systems

Role: AI Engineer (Freelance)

Stack: Python, RAG pipelines, hybrid retrieval, access control

Outcomes

  • LLM Knowledge Assistant (RAG) — internal document-to-knowledge system with search, RAG, citations, and access control to speed up retrieval and reduce time in recurring syncs
  • Document/knowledge workflows for finance and e-commerce clients
  • TODO: quantitative evals and regression metrics (not specified in source)

Context: Independent consulting (2022–2024). Led end-to-end delivery of applied LLM systems for finance and e-commerce — pipelines through production. Built document/knowledge workflows to speed up retrieval and decision-making; focused on quality, reliability, and latency/cost in production.

Evals and observability (regression suites, quality signals, tracing) are part of the stack; specific metrics for this engagement are TODO.