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 a developer or tech professional complete a {{use_case}} task. # Context - Pack: Developers & Tech Professionals - Category: Machine Learning & AI Engineering - Use case: Fine-Tuning Strategy Advisor - Source task: - 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 Fine-tuning vs. alternatives analysis, dataset requirements, technique recommendation, evaluation strategy, and cost estimate. # Constraints - Produce a complete, usable first draft in one response. - Avoid generic filler, vague advice, and unsupported claims. - Make the output specific, practical, and ready to use. # Output Fine-tuning vs. alternatives analysis, dataset requirements, technique recommendation, evaluation strategy, and cost estimate.
{{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 — fine-tuning is expensive, requires data curation, and creates a model maintenance burden.
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