How do you do RAG for LLMs?

To implement RAG (Retrieval-Augmented Generation) in Large Language Models (LLMs), follow these steps:

Data Preparation: Ensure your corpus is in a searchable format. If using Elasticsearch, index your data appropriately.

Model Selection: Choose your retriever and generator models. You can opt for pre-trained models or train your own based on your specific requirements.

Training: Train the retriever and generator models.

Integration: Combine the trained retriever and generator into a single RAG model.

Testing: Validate the model's performance using various metrics like BLEU for text generation quality and recall@k for retrieval accuracy.

To improve RAG performance in LLMs, leverage utility functions like get_retrieval_score(), which evaluates the effectiveness of the retriever by utilizing metrics such as Precision or NDCG (Normalized Discounted Cumulative Gain).

For a more comprehensive guide, refer to the LLM Models and RAG Hands-on Guide repository on GitHub, which provides a step-by-step guide for building conversational AI systems using large language models (LLMs) and RAG techniques.

Recommended resources:

• Implementing RAG With LLMs From Scratch: A Step-by-Step Guide (customgpt.ai)
• RAG Explained | Papers With Code (paperswithcode.com)
• Hands-On with RAG: Step-by-Step Guide to Integrating Retrieval and Generation (demir.io)
• LLM Models and RAG Hands-on guide (GitHub repository)