Need your help in making the AI Model responses more effective

A Hugging Face forum user asked how to improve a Google Gemini-Pro LLM product-query model built with the LangChain framework's Agent and Tools, and whether to switch to a Meta Llama model with fine-tuning. A respondent, Acehs, replied that Gemini is good and that prompts are not the important factor, suggesting instead a Python script for a proactive troubleshooting bot or a modelfile with Llama 3.1 and transformers connected to the user's API.

Is there any specific Prompt template for LLM to understand? In my case, I used Google Gemini-Pro LLM for building an AI model to answer user queries related to my product, I used LangChain framework to invoke my product APIs using Agent and Tools. How can I improve the performance of my model in terms of answering with actionable insights? Am I using the right model for LLM? Should I replace with Meta Llama LLM model? If so, how to do fine-tuning? Show drafts Acehs https://discuss.huggingface.co/u/Acehs 2 Gemini is good. Prompts isn’t the important thing. Assuming its for a product chat bot you could write a simple python script for giving the bot a proactive approach. call/answer for troubleshooting and accessing your llm for customer inquiry. or simply create a modelfile with llama3.1 with transformers to your API.