Let's discuss general approaches to working with the latest large language models. Accurately analyzing a model's capabilities and limitations is critical for efficient use. We can start by identifying the model's strengths and weaknesses. Then, let's compare which optimization methods are more effective in special data integration and fine-tuning processes. What strategies do you prefer?
How should we approach GPT-5?
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In your experience, does RAG or fine-tuning yield more efficient results for private data integration?