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Generative AI · Retrieval
Enterprise RAG / Document Intelligence
A retrieval-augmented generation system for grounding LLM answers in real enterprise documents.
Problem
Enterprise documents are unstructured, inconsistently formatted, and too large to fit into a model's context window — yet answers need to be accurate and traceable back to source.
Approach
Documents are parsed and chunked, embedded into a vector space, and indexed for similarity search. At query time, the most relevant chunks are retrieved and passed to an LLM as grounded context, so answers are backed by retrievable evidence rather than model memory alone.
Pipeline
- 01DocumentsRaw enterprise files
- 02ChunkingStructure-aware splitting
- 03EmbeddingsVector representation
- 04Vector DBpgvector similarity index
- 05RetrievalTop-k relevant context
- 06LLMGrounded generation
- 07AnswerTraceable to source
Technologies
- Python
- PyTorch
- LLMs
- Embeddings
- PostgreSQL
- pgvector
- Vector Databases
Notes
- Treats retrieval as an engineering problem — chunking strategy and embedding quality matter as much as the model.
- Designed to keep answers traceable back to source documents rather than opaque model output.