Understanding RAG
RAG enhances the accuracy and reliability of generative AI models with information fetched from external sources. It is a technique complementary to the internal knowledge of the LLMs. Before going into the details, let’s understand what RAG stands for:
- Retrieval: Search for relevant data
- Augmented: Add the data as context to the prompt
- Generation: Use the augmented prompt with an LLM for generation
Any LLM is bound to understand the data it was trained on, sometimes called parameterized knowledge. Thus, even if the LLM can perfectly answer what happened in the past, it won’t have access to the newest data or any other external sources on which it wasn’t trained.
Let’s take the most powerful model from OpenAI as an example, which, in the summer of 2024, is GPT-4o. The model is trained on data up to October 2023. Thus, if we ask what happened during the 2020 pandemic, it can be answered perfectly due...
