Case study · AI Engineering · Architecture
Enterprise RAG & LLM Platform
Production-grade RAG system with vector database, LLM integration, and automated evaluation using Ragas
The problem
Teams lose time searching scattered documents, and a plain chatbot makes up answers it cannot back with a source.
Constraints
- Answers must come from real documents
- LLM and embedding providers must be swappable without rewrites
- It has to be observable in production: metrics, logs and quality scores
Architecture and decisions
Click a box to see why it was chosen.
Why · Recursive split
Documents are split on natural boundaries (sections, paragraphs) so each chunk keeps its meaning.
Results
- automated tests
- 51
- automated tests
- retrieval methods fused
- 2
- retrieval methods fused
- embedding providers
- 3
- embedding providers
- Ragas quality metrics
- 3
- Ragas quality metrics
What I would do next
- 01Run the Ragas evals in CI and block releases that score lower
- 02Add a reranker after hybrid retrieval
- 03Show the source passages next to each answer in the API response