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AI engineering and production RAG

I design AI features as production software systems, with explicit data, retrieval, model, evaluation, security, and operational boundaries rather than treating a model call as the whole architecture.

What I can help with

  • Designing production RAG pipelines from document ingestion to cited answer generation
  • Combining lexical and vector retrieval with metadata filtering and reranking
  • Building grounding, citation, verification, confidence, and abstention paths
  • Evaluating retrieval and generation independently with representative test sets
  • Managing model integration, permissions, observability, latency, and cost in production

How I make decisions

AI output is probabilistic, so evaluation, observability, and safe fallback behaviour belong in the architecture from the start.

Reliable answers begin with trustworthy source ingestion, permissions, retrieval, and evidence—not prompt wording alone.

Models and providers should sit behind explicit boundaries so the system can be tested, compared, and changed deliberately.

Need this capability on your team?

Share the product context, current constraints, and outcome you need.

Discuss an engineering role