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Solving Production Issues In Modern RAG Systems-I
LLMs are great, but can we use them to answer our queries on our private data? This is where the Retrieval Augmented Generation or RAG comes in. RAG usage has been growing rapidly as most companies have a lot of proprietary data and they want their chatbots or other text-based AI to be specific to their company. RAG is a very interesting use case of LLMs, they are in direct competition with the increasing context length of LLMs, and I don’t know which one out of these two will prevail. But I’m positive that a lot of techniques that are developed to create better RAGs will be used in future systems, RAG might or might not be gone in a few years, but a few interesting techniques might inspire the next generation of systems. So, without further ado, let’s look into the details of creating next-generation AI systems.
Table of Contents
- What is RAG?
- Building a basic RAG Pipeline
- Overall Challenges
- 9 Challenges and Solutions to Modern RAG Pipelines
- Scalability
- Conclusion
Part 2: