Introduction to RAG

Large language models are powerful, but they suffer from several issues, including hallucinations, handling outdated or cut-off date data, and lack of transparency. To counter many of these challenges, RAG has emerged in the last couple of years as the solution to enhance and empower large language models and chatbot interactions. In a nutshell, RAG incorporates external databases, documents, or online sources on top of the pretrained models to augment the existing knowledge of the LLM.

The image below shows the various intersections of a user query or intention, large language models, and external documents or data sources. At the intersection of a query and an LLM, hallucinations are produced in reference to the LLM-generated response. The LLMs can generate inaccurate or misleading responses with high confidence.

At the intersection of large language models and external documents or data sources, we have fine-tuned LLM models. While fine-tuned LLM models are efficient and quite advanced in their responses because they are task-specific, they are not the go-to framework. Fine-tuning requires large amount of data to train the model, which might not be easy to acquire, they demand high computational power and GPUs to train the model, and they have a really high cost of re-training.

At the intersection of the three concepts is RAG, which combines the user query or intent, the pretrained large language model, and the combination of external documents or sources.

• RAG solves the LLM limitation when it comes to up-to-date information. LLMs are trained and deployed at a certain point in time, after which any new piece of data or information that happens after this date can’t be retrieved. This is where RAG facilitates the injection of new data into the pretrained LLM model without having to re-train the model.

• RAG was introduced in mid-2020, and it stands as a paradigm for enhancing generative tasks and advanced chatbots, and it is the optimal solution for dealing with LLM hallucinations. In the context of chatbots, hallucinations refer to the generation of responses that are not grounded in reality or the provided data. These can range from inaccuracies to completely fabricated statements, which can remove a user’s trust in the system. RAG not only updates and enriches the model’s knowledge without extensive retraining but also provides a mechanism to ground responses in verified information, thereby increasing accuracy and reliability.

The integration of RAG into LLMs has seen quick adoption. It has become a reference in refining chatbots' capabilities and rendering LLMs more viable for practical applications.