Currently working as a Solution Architect at MongoDB, I was inspired to write this article by engaging dialogues with my colleagues Fabian Valle, Brian Leonard, Gabriel Paranthoen, Benjamin Flast and Henry Weller.
Retrieval-augmented generation (RAG) represents a significant advancement in the field of generative AI, combining efficient data retrieval with the power of large language models.
At its core, RAG operates by employing vector search to mine relevant and existing data, combining this retrieved information with the user’s query, and then processing it through a large language model like ChatGPT.
This RAG method ensures that the generated responses are not just precise but also reflect current information, substantially reducing inaccuracies or “hallucinations” in the output.
However, as the landscape of AI applications expands, the demands placed on RAG are becoming more complex and varied. The basic RAG framework, while robust, may be no longer enough in addressing the nuanced needs of diverse industries and evolving use cases. This is where advanced RAG techniques come into play. These enhanced methods are tailored to cater to specific challenges, offering more precision, adaptability, and efficiency in information processing.
The Essence of Basic RAG
Retrieval-augmented generation (RAG) combines data management with intelligent querying to enhance AI’s response accuracy.
- Data preparation: It begins with the user uploading data, which is then ‘chunked’ and stored with embeddings, establishing a foundation for retrieval.
- Retrieval: Once a question is posed, the system employs vector search techniques to mine through the stored data, pinpointing relevant information.
- LLM query: The retrieved information is then used to provide context for the Language Model (LLM), which prepares the final prompt by melding the context with the question. The result is an answer generated based on the rich, contextualized data provided, demonstrating RAG’s ability to produce reliable, informed responses.