Rag

Diagnose AI application failures caused by oversized, incomplete, or poorly prioritized model context.

Diagnose retrieval quality problems caused by inconsistent embedding models, dimensions, preprocessing, or indexing pipelines.

How embeddings represent data as vectors and how similarity search supports retrieval systems.

Evaluate retrieval, generation, safety, reliability, latency, and cost instead of relying on a single model score.

Core security controls for LLM, RAG, and agentic AI applications.

A practical workflow for diagnosing weak semantic retrieval in AI applications.

A practical framework for evaluating LLM, RAG, and agent systems before and after production release.

A requirements-first architecture pattern for designing a production retrieval-augmented generation platform.

Diagnose unsupported RAG answers by separating retrieval, context construction, model behavior, and evaluation failures.

A production-oriented introduction to retrieving external information and using it as context for generative AI responses.