What is RAG (Retrieval-Augmented Generation)? - Definition & Meaning
RAG (Retrieval-Augmented Generation) combines information retrieval with AI text generation for more accurate answers. Learn how RAG works.
Definition
RAG (Retrieval-Augmented Generation) is an AI architecture pattern that combines the power of information retrieval with the generative capabilities of a language model. Instead of purely relying on the model's training data, RAG retrieves relevant documents from an external knowledge source and uses them as context for generating more accurate answers.
Technical Explanation
A RAG pipeline consists of three steps: indexing (documents are converted to embeddings and stored in a vector database), retrieval (the user query is converted to an embedding and the most relevant documents are retrieved via similarity search), and generation (the retrieved documents are provided as context to the LLM). Chunking strategies determine how documents are divided. Re-ranking improves the relevance of retrieved results. Hybrid search systems combine dense retrieval (embeddings) with sparse retrieval (BM25/keyword search) for better recall.
How Refront Uses This
Refront uses RAG to give AI agents access to project-specific knowledge. When an agent picks up a ticket, relevant documents, previous tickets, and codebase information are retrieved to enrich the context. This results in more accurate and project-relevant output than a generic LLM would provide.
Examples
- •The AI agent searches project documentation via RAG to resolve a ticket based on existing architecture decisions.
- •RAG retrieves previously similar tickets so the AI can learn from how the team solved them before.
- •When generating a quote, RAG uses historical project data to provide realistic time estimates.
Frequently Asked Questions
Why is RAG better than just using an LLM?
An LLM can only answer based on its training data, which may be outdated or incomplete. RAG adds current, domain-specific information as context, making answers more accurate, relevant, and less prone to hallucination.
What is the difference between RAG and fine-tuning?
Fine-tuning permanently adjusts the model's weights on new data, while RAG dynamically retrieves data during answer generation. RAG is more flexible because the knowledge source can be easily updated without retraining the model.
What types of documents can be used in a RAG system?
RAG can work with virtually any text format: documents, wiki pages, code, emails, ticket descriptions, chat logs, and more. The documents are first converted into embeddings and stored in a vector database.
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