RAG Fundamentals

Introduction

Language models only know what appears in their training data. When you need answers about proprietary documents or private information, they fall short. RAG, or Retrieval Augmented Generation, solves this problem. It combines document search with a model’s text generation capabilities, allowing the AI to answer questions based on your own content.

RAG in a Nutshell

RAG works in two steps. First, the system searches for document sections that match your question. Then it sends those sections along with your question to a language model. The model generates its answer based on the information provided.

This approach offers two key benefits. The model doesn’t need to memorize your documents, and your answers remain verifiable because sources are visible.

Key Terms and Components

Term	Definition
Retrieval	Finding document sections relevant to a query
Generation	Producing an answer via the language model
Embeddings	Numerical vectors that represent text content
Vector database	Storage system that saves and searches documents as vectors
Chunking	Dividing documents into meaningful sections
Context	The document sections passed to the model

Practical Applications

When is RAG worthwhile?

RAG makes sense whenever you need to query your own documents. Use cases include internal knowledge bases, contract management, handbooks, research papers, and customer documentation. Instead of squeezing entire documents into the context window, RAG extracts only the relevant passages.

What does a typical workflow look like?

1. Load documents and split them into sections.
2. Generate an embedding vector for each section.
3. Store vectors in a database.
4. Search for a matching vector when a question arrives.
5. Pass the found sections and the question to the model.
6. The model generates an answer and cites sources.

Advantages and Disadvantages

Advantage	Disadvantage
Current knowledge from your own documents	Database setup and maintenance required
Verifiable sources	Quality depends on chunking and embeddings
Smaller context window footprint	Extra components like vector databases needed
Lower model size requirements	Poor chunking leads to poor answers

Hardware, Cost, and Security Considerations

RAG requires an embedding model running locally and storage space for the vector database. Both are relatively lightweight compared to large language models. Costs stay reasonable since open-source models and free vector databases like Chroma or Qdrant are available.

Security is a major advantage. Your documents never leave your machine, which matters especially for businesses handling sensitive data.

More on AI Topics

• RAG combines document search with language model responses.
• Key components include chunking, embeddings, and vector databases.
• Good chunking and embeddings are critical for quality answers.
• Local RAG keeps data in-house.

Learn more about the technology in Chunking and Embedding Models. For vector databases, see Chroma.

FAQ - Common RAG Questions

Does RAG require a lot of storage?

The vector database itself is usually small. Storage costs come mainly from the original documents. A few gigabytes of storage typically suffice for thousands of documents.

Can RAG handle PDFs?

Yes, with appropriate parsers or OCR. Most RAG pipelines support PDFs, Word documents, and web pages.

Must the language model be trained on the documents?

No. The model receives relevant passages in the prompt. It only needs to understand and summarize them.

What’s better: long context or RAG?

For large document collections, RAG is usually more efficient. Long contexts consume significant memory and become less accurate as they grow longer.

Tools and Further Reading

For getting started, consider Open WebUI with RAG functionality, Chroma, Qdrant, LangChain, and LlamaIndex. Find embedding models on Hugging Face.

Sources

• LangChain RAG Tutorials
• Qdrant Documentation
• Hugging Face Embedding Models