Production-Grade RAG System
Retrieval-augmented generation (RAG) pipeline designed for real documentation: security reports, infrastructure runbooks, and architecture diagrams. The focus is reliability, debuggability, and evals.
Architecture
- Document ingestion from files and APIs.
- Chunking tuned for task: by heading, by semantic similarity, or fixed size.
- Vector store using FAISS / Chroma-like backends.
- Embeddings from OpenAI / Claude-style models.
- Top-k and MMR style retrieval for context selection.
Evaluation & Metrics
- Context hit rate on ground-truth QA pairs.
- Answer correctness judged by a separate LLM grader.
- Failure case logging: missing context, irrelevant chunks, hallucinations.
Usage
The RAG stack is designed to slot into AI assistants like Black Halo or SOC-style copilots, giving them grounded context for their reasoning while preserving traceability back to original documents.