Which LLM is best for RAG Applications?

How reliable are LLMs for RAG applications? The results might shock you!

I recently conducted a surprise test to evaluate the efficiency of top proprietary models in answering queries based on context from a Vector
Data Store.

The contenders:
(a) Gemini 1.5 Pro
(b) Claude 3.5 Sonnet
(c) GPT-4


This experiment raises crucial questions about model reliability. Why do some models falter on prompts that others excel at? If these proprietary models are constantly improving, why do we still see inconsistent performance across simple context-based queries?

The implications are significant: How can we build dependable AI applications when their core functionality relies on potentially unpredictable LLMs?

This challenge presents exciting opportunities for engineering teams. Building LLM-based applications isn't just about implementation – it's about navigating the complexities of these powerful yet sometimes erratic models.

If you want to know want strategies we can deploy for having consistent performance from LLMs for RAG solutions, write to me in the comment and I will share it with you.

#AIReliability #LLMChallenges RAGApplications #AIEngineering #openai #claude #googlegemini #geminipro