LLM Fine-Tuning Strategist

Guides decisions on when and how to fine-tune large language models, covering LoRA/QLoRA, dataset curation, RLHF/DPO alignment, evaluation strategies, and cost-performance tradeoffs for production deployments.

Gold

v1.0.00 activationsAI & Machine LearningTechnologyexpert

SupaScore

Research Quality (15%)

8.5

Prompt Engineering (25%)

8.5

Practical Utility (15%)

Completeness (10%)

8.5

User Satisfaction (20%)

Decision Usefulness (15%)

Best for

▸Deciding between LoRA vs full fine-tuning for domain adaptation tasks
▸Designing RLHF pipelines for AI safety alignment in production systems
▸Optimizing QLoRA configurations for memory-constrained GPU environments
▸Evaluating ROI of fine-tuning vs RAG for enterprise knowledge injection
▸Building synthetic training datasets using Self-Instruct methodologies

What you'll get

●Structured decision framework comparing LoRA vs full fine-tuning with specific parameter recommendations and cost estimates
●Step-by-step training pipeline design with dataset curation strategy, hyperparameter ranges, and evaluation benchmarks
●RLHF implementation roadmap with reward model architecture and human feedback collection workflow

Not designed for ↓

×Teaching new factual knowledge to models (use RAG instead)
×Basic prompt engineering or few-shot examples
×Training models from scratch or pre-training
×Fine-tuning proprietary models like GPT-4 (API limitations apply)

Expects

Clear problem definition including current model performance gaps, budget constraints, and specific behavioral changes needed from the model.

Returns

Detailed fine-tuning strategy with method selection, dataset requirements, evaluation metrics, and cost-performance projections.

Evidence Policy

Enabled: this skill cites sources and distinguishes evidence from opinion.

llmfine-tuningloraqlorarlhfdpoalignmentpefttraining-datasynthetic-datamodel-evaluationtransfer-learning

Research Foundation: 8 sources (5 academic, 2 official docs, 1 paper)

This skill was developed through independent research and synthesis. SupaSkills is not affiliated with or endorsed by any cited author or organisation.

Version History

v1.0.02/14/2026

Initial release

Works well with

AI Cost OptimizerPlatinum Dataset Curation SpecialistGold LLM Evaluation Framework DesignerGold Model Deployment OptimizerPlatinum PyTorch Deep Learning EngineerPlatinum

Need more depth?

Specialist skills that go deeper in areas this skill touches.

LoRA Fine-Tuning SpecialistGold LoRA Dataset CuratorGold PyTorch Lightning EngineerGold

Common Workflows

Production Fine-tuning Pipeline

End-to-end workflow from fine-tuning strategy through dataset creation, training execution, and production deployment

llm-fine-tuning-strategist→Dataset Curation Specialist→PyTorch Lightning Engineer→Model Deployment Optimizer

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