# RAG Systems (Retrieval-Augmented Generation)

> Retrieval-augmented generation systems with citation accuracy and grounded answers: RAG development from data pipeline to interface, built by Sentient Arc.

Retrieval-augmented generation systems that answer from your documents, with citations, not hallucinations, from the data pipeline to the interface.

## A model that does not know your business
A base language model is fluent and confidently wrong about your domain. Ask it about your contracts, filings, or product and it invents plausible answers, unusable where accuracy is the point.

Stuffing documents into a prompt does not fix it. Real retrieval is an engineering problem: chunking, embeddings, ranking, and grounding.

## Grounded answers, by construction
We build the full RAG pipeline: parse and chunk your sources, embed and index them, retrieve the most relevant passages, and constrain the model to answer only from what it found, with citations back to the source.

We tune retrieval against real queries and add reflection loops that verify citations before an answer ever reaches a user.

## What we build
- **Ingestion pipelines**: Parsing, OCR, and recursive chunking that survive nested tables and long documents.
- **Vector search**: Embeddings, hybrid retrieval, and re-ranking tuned to your real questions.
- **Grounded generation**: Answers constrained to retrieved context, with citations and self-checks against hallucination.
- **Knowledge interfaces**: Conversational and search UIs that make a corpus instantly queryable.

## Stack
- **Retrieval:** Pinecone, pgvector, Hybrid search, Re-rankers
- **Models:** Claude, OpenAI, Embeddings, LangChain
- **Pipeline:** Parsing & OCR, Chunking, Evals, Streaming

## FAQ
**What is RAG (retrieval-augmented generation)?**

RAG grounds a language model in your own documents. Instead of answering from training data, the system retrieves the most relevant passages from your knowledge base and answers from those, with citations, so responses stay accurate and auditable.

**Does RAG stop hallucinations?**

It sharply reduces them. By constraining the model to retrieved context and verifying citations before responding, answers stay anchored to your sources rather than the model's imagination.

**What can you build RAG over?**

Contracts, filings, policies, tickets, wikis, product docs: any corpus where people need fast, cited answers instead of manual search.

**How do you measure RAG quality?**

With evals on real queries: retrieval relevance, answer faithfulness, and citation accuracy, so improvements are measured, not guessed.

---
Canonical page: https://www.sentientarc.com/capabilities/rag-systems