What is retrieval-augmented generation (RAG)?
Retrieval-augmented generation (RAG) is an AI technique that enhances LLM outputs by retrieving relevant information from external knowledge sources and including it in the prompt context. It grounds AI responses in real, current data rather than relying solely on training data.
RAG Components
How RAG works
Every feature designed to help your team work smarter with AI.
Knowledge retrieval
A retrieval system searches your organization's documents, databases, or knowledge bases for information relevant to the user's query.
Context augmentation
Retrieved information is formatted and added to the prompt context so the LLM can reference it when generating a response.
Grounded generation
The LLM generates responses based on both its training knowledge and the retrieved context, reducing hallucinations.
Prompt engineering for RAG
Effective RAG requires well-structured prompts that instruct the model how to use retrieved context and when to cite sources.
Iterative improvement
RAG systems improve through prompt optimization, retrieval tuning, and feedback loops that refine both components.
Data control
RAG lets you control what information the model accesses, keeping sensitive knowledge within your security perimeter.
Benefits
Why RAG matters for AI teams
FAQ
Frequently asked questions
How is RAG different from fine-tuning?
Fine-tuning changes the model's weights by training on your data. RAG keeps the model unchanged and provides relevant data at inference time through the prompt. RAG is faster to implement and keeps your data separate from the model.
How does TeamPrompt help with RAG workflows?
TeamPrompt helps teams build and share effective RAG prompt templates that structure how retrieved context is presented to AI models. Good RAG prompt engineering is essential for output quality.
Does RAG eliminate hallucinations?
RAG significantly reduces hallucinations for topics covered by your knowledge base, but does not eliminate them entirely. Effective prompt engineering — instructing the model to cite sources and acknowledge uncertainty — further improves reliability.
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