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

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

Retrieval Augmented Generation (RAG) is a highly misunderstood but indispensable concept in modern AI, and I'm thrilled to share some insights about it. A big shoutout to Pinecone for sponsoring this video and partnering exclusively with my channel. Pinecone offers an incredible vector database product, which is central to RAG's functioning.

Misunderstanding of Fine-Tuning and Additional Knowledge

One common misconception about large language models (LLMs) is how to provide them with additional knowledge. Many people think they need to use fine-tuning. While fine-tuning is often employed to adjust a model's tone or manner of response, it is not ideal for adding external knowledge. Surprisingly, nine out of ten times, when you think you need fine-tuning, what you actually need is RAG.

What is RAG?

RAG stands for Retrieval Augmented Generation. It essentially gives large language models access to external information sources to augment your prompt. I view RAG in two primary ways:

A fast and efficient means to provide LLMs with additional knowledge.
A method to give LLMs long-term memory, which they inherently lack.

Large Language Models and Context Windows

LLMs like GPT-4 are static once trained, meaning they do not gain new information unless explicitly provided. One way to provide this additional knowledge is through the prompt itself. However, the context window— the number of words or tokens that can be included in a prompt and response—is limited. For example, GPT-4 can handle up to 128,000 tokens, but this is used up quickly, making it inefficient and costly for continuous information updates.

RAG to the Rescue

To illustrate, let's consider building a customer service chatbot that stores all conversations indefinitely. Without RAG, every interaction would have to be fed back into the prompt, quickly maxing out the context window. Similarly, if you need to provide the LLM with internal company documents, stuffing those into every prompt is not scalable.

How Does RAG Work?

RAG involves storing information (documents) externally and allowing the LLM to query those documents. For example, Tesla’s new earnings report can be stored in a RAG database. When a query about Tesla earnings is made, the relevant document parts are retrieved and appended to the prompt. This targeted approach is far more efficient.

Detailed Workflow of RAG

Without RAG, say you ask a generative AI, "How do I turn off the automatic reverse braking on the Volvo XC60?" The model might hallucinate and provide an inaccurate answer. However, with RAG, the process involves:

Sending the Volvo user manual to an embedding model.
Storing embeddings in a vector database.
When a query is made, it is transformed into an embedding.
The vector database finds related information.
This information is appended to the prompt for an accurate response.

Embeddings and Vector Spaces

Embeddings convert text into a series of numbers, placing them in a multi-dimensional space. Words or phrases close in meaning will be near each other in this vector space.

Pinecone’s Role

Pinecone excels at high-scale, efficient vector storage and retrieval. Developers can use Pinecone to handle embeddings and vector spaces without needing in-depth knowledge of how RAG functions.

Conclusion

RAG is a game-changer in providing relevant external knowledge to LLMs, making them more efficient and reducing hallucinations. Pinecone's platform simplifies setting this up. If you're interested in a hands-on tutorial or deeper dive into RAG, let me know in the comments.

Keywords

Retrieval Augmented Generation
Pinecone
Vector Database
Large Language Models
Context Window
Embeddings
Hallucinations
Fine-Tuning

FAQ

Q: What is Retrieval Augmented Generation (RAG)? A: RAG is a technique that allows large language models to access external information sources to augment the prompt, making them more knowledgeable and reducing hallucinations.

Q: How is RAG different from fine-tuning a model? A: Fine-tuning adjusts a model's response style or tone, while RAG provides the model with additional relevant information from external sources.

Q: What are embeddings in the context of RAG? A: Embeddings convert text into numerical vectors, placing words or phrases in a multi-dimensional space to find related information efficiently.

Q: Why is Pinecone recommended for RAG implementations? A: Pinecone provides a highly efficient and scalable vector database, making it easy to store and query embeddings, essential for RAG.

Q: Can RAG be used for applications other than customer service chatbots? A: Yes, RAG is versatile and can be used in many applications requiring real-time access to external information, such as financial reports, scientific documents, and more.

Q: What is a context window in LLMs? A: The context window is the limit on the number of tokens (words or parts of words) that can be included in a prompt and the LLM's response.