What is RAG?
Remember back in 2023 when you used ChatGPT before it had web browsing and asked about current events, only to get a generic response or a note about its knowledge cutoff?
Remember how frustrating that was?
Well, that’s the limitation RAG has solved. By retrieving fresh or domain-specific data in real time, RAG allows models to stay current and accurate without retraining.
Retrieval-Augmented Generation (RAG) is a technique for enhancing generative AI models by feeding them relevant external information within workflows.
Instead of relying solely on an LLM’s static training, a RAG system retrieves up-to-date or domain-specific data from an external source (like a document database or API) and augments the model’s input prompt with that data. The LLM then generates an answer grounded in this retrieved context.
By combining the LLM’s parametric knowledge (patterns learned in its weights) with non-parametric memory (information retrieved on demand), RAG systems deliver responses that are fluent, contextually accurate, and verifiable.
RAG was proposed in a 2020 research paper, predating the widespread adoption of mainstream LLMs.
Today, RAG is an industry-accepted standard for improving the outputs of LLMs.
Implementing a basic RAG pipeline is relatively straightforward: a user query is converted into a vector embedding, which is then used to retrieve relevant documents from a vector database or search API. These documents (or context snippets) are prepended to the prompt sent to the LLM, enabling it to generate responses grounded in both the original question and the retrieved information.
This simple yet powerful workflow transforms generic LLMs into more context-aware systems capable of producing timely, accurate, and trustworthy outputs.
The RAG retrieval step happens at query time. Unlike fine-tuning or training new parameters, RAG doesn’t alter the model’s weights; it injects knowledge into the model’s input. This means you can update the knowledge base (e.g., add new documents or data) and the system will immediately use the new information in responses – no model retraining needed.
RAG is a more efficient way to keep a model informed and domain-aware, making AI outputs more accurate, trustworthy, and tailored to real-world needs.
RAG addresses some of the most significant limitations of standalone LLMs. By grounding model outputs in external data, RAG brings a host of benefits: