What is Retrieval-Augmented Generation (RAG)?
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AI Development SolutionsWhat is RAG?
Retrieval-Augmented Generation (RAG) is an architectural approach for large language models that optimizes output by referencing an external knowledge base before generating a response. This mitigates hallucinations and bypasses the limitations of static training data.
How does RAG work?
The RAG process consists of several steps: first, the system receives a user query, then it performs retrieval across a vector database or other sources to find relevant information snippets. Afterward, the retrieved context is augmented with the original query and passed to the language model, which generates the final response.
Use Cases
Building smart assistants that can answer employee questions using internal company documentation.
Automatically extracting information from large volumes of legal or technical documents without needing to fine-tune the model.
/ FAQ
RAG doesn't change the model itself; it only adds context to the prompt. Fine-tuning adjusts the model's weights based on new data. RAG is cheaper and easier to maintain for up-to-date knowledge.
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