Pipeline design
Production retrieval is a pipeline: ingestion, chunking, metadata, embeddings, lexical search, vector search, reranking, and answer evaluation.
Retrieval Infrastructure
Hybrid search, embedding pipelines, metadata strategy, reranking, and evals for RAG systems in production.
retrieval architecture diagram. Nodes: docs, chunks, vector, lexical, rerank, eval. Flow: docs to chunks (primary); chunks to vector (primary); vector to rerank (primary); chunks to lexical (supporting); lexical to rerank (supporting); rerank to eval (feedback).
Diagnostics
Before changing models, inspect misses, ambiguous chunks, stale documents, filters, and query intent.
Retrieval Infrastructure
Embedding pipelines, hybrid search, metadata filters, reranking, and stable eval sets for RAG systems.
retrieval architecture diagram. Nodes: docs, chunks, vector, lexical, rerank, eval. Flow: docs to chunks (primary); chunks to vector (primary); vector to rerank (primary); chunks to lexical (supporting); lexical to rerank (supporting); rerank to eval (feedback).
Evaluation Systems
Regression suites, adversarial cases, trace review, cost tracking, and evidence that a system is ready to ship.
evals architecture diagram. Nodes: trace, cases, score, regress, report, fix. Flow: trace to cases (primary); cases to score (primary); score to report (primary); score to regress (feedback); regress to fix (supporting); fix to trace (loop).
Related projects
Shipped systems where this concept runs in production.
Have a project in mind?
Tell me about the operational problem you want to solve. A few lines about the context and the systems involved are enough for a first assessment.