Retrieval Augmented Generation (RAG): Boosting LLM Performance with External Knowledge

To use RAG, first convert the external data into numerical representations using embedded language models. Then, append the relevant context from the knowledge base to the user's prompt. Finally, feed the prompt to the LLM to generate a response.

Foundation models are typically trained offline, which makes them frozen in time and unaware of the current data. They may also lack effectiveness in domain-specific tasks due to their general training.

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Retrieval Augmented Generation (RAG) addresses these challenges by retrieving external data from various sources, such as documents, databases, or APIs. It then incorporates that data into prompts for large language models (LLMs). This allows LLMs to generate more accurate and informative responses, even for complex or domain-specific tasks.

RAG has a number of advantages over traditional LLM-based approaches:

- It can be used to generate more accurate and informative responses, even for complex or domain-specific tasks.
- It can be personalized for specialized domains, such as medicine, law, and many more.
- It can be used with a variety of external data sources, including documents, databases, and APIs.
- Knowledge libraries can be updated independently to keep information current.
- RAG is still under development, but it has the potential to revolutionize the way we use LLMs.

Key Takeaways:
– RAG methods enhance model performance by incorporating external data into prompts.
– RAG can be personalized for specialized domains like medicine, law, and many more.
– External data sources can include documents, databases, or APIs.
– Knowledge libraries can be updated independently to keep information current.

Table of Contents:
0:00 – Introduction to RAG
3:42 – Large Language Models
7:03 – Retrieval Augmented Generation
23:06 – Pros and Cons of RAG
24:02 – Demo
40:21 – QnA

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[dealwithdata]

Can you please locate me to the python notebook file which you showed in demo