RAG — Retrieval-Augmented Generation

Why RAG?

Large language models are trained on public data up to a fixed cutoff date. They cannot know about your internal documents, product catalog, latest policies, or proprietary research. RAG bridges this gap by retrieving the most relevant context from your data at query time and feeding it to the model before it generates a response.

The result: accurate, citable, up-to-date answers — without the cost and complexity of fine-tuning.

RAG Architecture Components

• Document Ingestion Pipeline — Load, parse, chunk, and embed documents from any source: PDFs, databases, APIs, wikis, CRMs.
• Vector Store — Store and index embeddings for fast semantic similarity search at scale.
• Retrieval Strategy — Dense retrieval, sparse retrieval (BM25), or hybrid approaches for optimal precision and recall.
• Re-ranking — Cross-encoder re-ranking to surface the highest-quality context before passing to the LLM.
• Generation & Citation — LLM response generation with source attribution so users can verify every answer.
• Evaluation Framework — Automated RAGAS-based evaluation to measure faithfulness, relevance, and groundedness.

Use Cases We've Built For

Advanced RAG Techniques

Basic RAG gets you started, but production systems demand more. We implement advanced patterns including:

• → Agentic RAG with query decomposition and multi-hop retrieval
• → Contextual compression to reduce noise in retrieved chunks
• → Parent-child chunking strategies for better context windows
• → Metadata filtering for access-controlled, role-aware retrieval
• → Incremental index updates without full re-embedding