Improve LLM Performance by Maximizing Iteration Speed

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LLM Application development is extremely iterative, more so than any other types of development. This is because in addition to all the activities involved in regular application development, we also need to make the LLM Application accurate and reduce hallucination. To improve performance, we need to trial and error various combinations of LLM models, prompt templates (e.g., few-shot, chain-of-thought), prompt context with different RAG architecture, try different agent architecture, and more.There are thousands of permutations to try. We need to be able to easily experiment with these different permutations, measure performance in an objective way, and compare performance across each other

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