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## Results from Experiments Evaluating GPT-4, Claude 2.1, and Claude 3.0 Opus

### Introduction
Recent evaluations of RAG systems focus on the retrieval stage, but the generation aspect is equally important. The ability of a model to synthesize retrieved information into complex responses is crucial in real-world applications.

### Methodology
We conducted experiments to compare GPT-4, Claude 2.1, and Claude 3 Opus in terms of their generation capabilities. The results and model nuances encountered are detailed in this article.

### Importance of Generation Phase
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### GitHub Repository
All the data and methods used in the experiments are available in a GitHub repository for reproducibility.

### Takeaways from Tests
Our tests revealed that prompt engineering plays a significant role in the performance of models like GPT-4 and Claude. Adding strategic prompts can enhance a model’s performance across various evaluations.

### Impact of “Explain Yourself” Prompt
The addition of the “explain yourself” prompt drastically improved GPT-4’s performance, showcasing the influence of prompt wording on model accuracy.

### Synthesizing Information
RAG systems excel in synthesizing retrieved information, filling gaps, and presenting coherent responses. The generative step is crucial for intelligent and adaptive responses in real-world applications.

### Further Evaluation
Further tests were conducted to assess the models’ ability to transform and synthesize information in various formats, highlighting their strengths and weaknesses.

### Implications for Model Evaluation
The evaluation of Language Models should consider factors beyond correctness, such as response length. The verbosity of a model’s responses can influence its perceived performance and should be taken into account in evaluations.

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