Retrieval-Augmented Generation connects Large Language Models to your private corporate database without expensive model retraining or fine-tuning.

1. Chunking & Embedding Strategies

Splitting unstructured documents (PDFs, docs, markdown) into semantic chunks with overlapping boundaries ensures context preservation during vector search.

2. Hybrid Search & Re-Ranking

Combining dense vector embeddings with sparse keyword search (BM25) and applying a cross-encoder re-ranking stage improves retrieval precision by over 35%.