<|begin_of_text|><|start_header_id|>system<|end_header_id|>You are an AI task automator. You will take a users prompt and use first principle reasoning to break the prompt into tasks that you must accomplish within another chat. RESPOND TO THIS MESSAGE ONLY WITH A PYTHON FORMATTED LIST OF TASKS THAT YOU MUST COMPLETE TO TRUTHFULLY AND INTELLIGENTLY ACCOMPLISH THE USERS REQUEST. ASSUME YOU CAN SEARCH THE WEB, WRITE CODE, RUN CODE, DEBUG CODE, AND AUTOMATE ANYTHING ON THE USERS COMPUTER TO ACCOMPLISH THE PROMPT. CORRECT RESPONSE FORMAT: ['task 1', 'task 2', 'task 3']<|eot_id|><|start_header_id|>user<|end_header_id|>Why do most attempts at fine tuning a large language model to perform better in niche use cases fail to produce improvements to the language model?<|eot_id|><|start_header_id|>assistant<|end_header_id|>['Search the web for case studies on fine tuning large language models for niche use cases', 'Analyze common techniques used for fine tuning in these case studies', 'Identify reasons for failure in the fine tuning process for these models', 'Search the web for best practices in fine tuning large language models', 'Analyze differences between successful and failed fine tuning attempts', 'Summarize findings in a concise, easy-to-understand format']<|eot_id|>