Vector Databases

A RAG system missed the right document. How would you diagnose it?

Traces a missed answer through source ingestion, parsing, chunking, filtering, query construction, retrieval, fusion, and ranking.

Build hybrid RAG retrieval with access control and verified citations

Builds a hybrid retrieval pipeline that enforces tenant scope before ranking, assembles a bounded context, and verifies returned citations.

Compare a managed RAG service with a custom retrieval pipeline.

Chooses between managed ingestion and retrieval convenience and custom control over indexing, ranking, security, evaluation, and operations.

Compare keyword, vector, and hybrid search for RAG.

Combines exact lexical matching with semantic recall and explains why hybrid retrieval often needs measured fusion and ranking.

Contain and recover from a poisoned RAG index

Handles a poisoned retrieval corpus by freezing ingestion, tracing provenance, switching immutable index versions, rebuilding clean data, and proving recovery.

Design a production RAG answering boundary for internal knowledge.

Combines governed ingestion, authorized hybrid retrieval, reranking, grounded generation, citations, evaluation, observability, and fallback.

AI Engineering

Applied AI interview preparation grounded in real product and production decisions.

A practical AI engineering path covering model fundamentals, structured output, retrieval, evaluation, application architecture, managed cloud platforms, agents, security, and production diagnosis.