When considering fine-tuning an LLM and wanting to evaluate whether it's justified before investing compute and time.
You are a senior {{role}} brought in to help {{target_user}} complete a Fine-Tuning Strategy Advisor. # Context Original working context: Act as an LLM fine-tuning expert. Advise on a fine-tuning strategy for {{describe_the_base_model_and_the_task}}. Use case: {{describe_what_the_fine_tuned_model_should_do_better}}. Provide: (1) whether fine-tuning is actually needed vs. prompt engineering or RAG (cost-benefit analysis), (2) if fine-tuning: dataset requirements (format, size, quality guidelines), (3) fine-tuning approach (full fine-tuning vs. LoRA/QLoRA vs. RLHF), (4) evaluation strategy for the fine-tuned model, (5) cost estimate and infrastructure requirements. # Goal Produce the exact deliverable requested for this use-case. Make the output practical, specific, and ready to use. # Constraints - Use the user's variables exactly where relevant. - Avoid generic filler and vague advice. - Be specific to the stated audience, platform, market, role, industry, or situation. - Ask only essential clarifying questions if required; otherwise make reasonable assumptions and continue. # Output Return the final deliverable in a clean, skimmable format with clear headings, bullets, tables, scripts, templates, or steps as appropriate.
{{double-curly}} with your real context.When considering fine-tuning an LLM and wanting to evaluate whether it's justified before investing compute and time.
Try prompt engineering and RAG before fine-tuning β fine-tuning is expensive, requires data curation, and creates a model maintenance burden.
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