How to give AI "Memory" - Intro to RAG (Retrieval Augmented Generation)

The video introduces Retrieval Augmented Generation (RAG) as a method to enhance large language models by providing external sources of information stored in vector databases, allowing for more accurate and informed responses. RAG overcomes the limitations of traditional methods by continuously updating and expanding the knowledge available to models like GPT-4, improving their long-term memory and ability to generate sophisticated answers.

In the video, the concept of Retrieval Augmented Generation (RAG) was introduced as a method to give large language models additional knowledge and long-term memory. RAG involves providing external sources of information to augment prompts given to these models. The traditional method of fine-tuning models is often misunderstood, with RAG being a simpler and more efficient way to provide additional knowledge to large language models. Large language models are described as being “frozen in time” after training, meaning they do not receive additional information unless explicitly given to them. This limitation highlights the need for methods like RAG to continuously update and expand the knowledge available to these models.

The limitations of the context window in large language models were discussed as a key reason why RAG is preferred over traditional methods. Context window refers to the number of words or tokens that can be included in a prompt and response, and it is limited in large language models. As models like GPT-4 have context windows that can quickly be used up, RAG becomes a more efficient way to provide additional knowledge without overwhelming the context window. An example of building a chatbot for customer service highlighted how RAG can help store conversations over time without filling up the context window with irrelevant information.

RAG was explained as a process of storing external information in a vector database and allowing the large language model to query this information when needed. By using RAG, models like GPT-4 can access up-to-date information, such as recent earnings reports, even if they were not part of the model’s original training data. The video detailed a step-by-step process of how RAG works, from converting text documents into embeddings to querying the vector database to retrieve relevant information for prompts. This process enables large language models to generate more accurate and informed responses.

The video discussed how RAG is not only beneficial for large language models but also for agents that require external knowledge sources to provide more sophisticated answers. By utilizing RAG, agents can iteratively search for information, incorporate external knowledge, and deliver better responses to queries. Pine Cone’s vector database was highlighted as a key tool for implementing RAG efficiently, as it can store and retrieve vast amounts of data in a lightning-fast manner. The video emphasized that developers do not need to have an in-depth understanding of vector storage to leverage RAG, as tools like Pine Cone simplify the process.

In conclusion, the video introduced RAG as a powerful method for giving large language models additional knowledge and long-term memory. By utilizing external sources of information stored in vector databases, RAG enables more accurate and informed responses from large language models. The limitations of context windows in traditional methods make RAG a preferred approach for continuously updating and expanding the knowledge available to these models. The video highlighted the ease of implementing RAG with tools like Pine Cone, making it accessible for developers to enhance the capabilities of their language models and agents.