Mastering the RAG Pipeline: Tips and Tricks for Success
The Retriever-Augmented Generation (RAG) pipeline is a powerful tool in the world of Natural Language Processing. It combines the strengths of retrieval-based and generative models to provide more accurate and contextually relevant responses. Looking at the popularity of RAG based applications, it is important to understand and solve few of the problem areas.
In this article, I’ll share some of the common issues which we are facing today and how we can solve those with some possible solutions.
Here are some tips and tricks to help you make the most of the RAG pipeline.
Tips and Tricks for Using the RAG Pipeline
1. How to extract the correct content?
2. How to solve the problem of missing top ranked documents?
Check this video to know 7 different ways to solve this problem.
3. How to extract the correct content from LLM response?
Check this video to know 7 different ways to extract LLM response in correct output format.
4. What to use LLMChain or from_llm?
These are are very confusing terms and does nearly same except one. Check this video to know about that one thing which make these 2 different from one another.
Conclusion
The RAG pipeline is very powerful and effective, only if it is used correctly. By understanding how the RAG pipeline works and applying these tips and tricks, you can make the most of this innovative technology.
Stay tuned for more such tips as I’ll keep updating this post.
