RAG and Knowledge Retrieval
Building retrieval systems that connect language models with documents, CMS content, metadata and vector search.
Capability statementI design retrieval pipelines where grounding, not just embedding similarity, is the thing being optimized for.
Full description
I work with retrieval-augmented generation systems that index structured and unstructured content, retrieve relevant context and generate grounded answers. My focus is not only on embeddings, but also on chunking, metadata, filtering, relationships, evaluation and retrieval transparency.
Evidence standard
Strengths
- Content-type-agnostic normalization (new CMS types need zero retrieval code changes)
- Keyword-scoring fallback when the vector store is unavailable, so retrieval degrades gracefully instead of failing
Current focus
Making sure new content types (Expertise, Skills) index and retrieve correctly without any change to the normalization or embedding code.