# Fine Tune > Use when you need to fine-tune(ファインチューニング) and optimize LangGraph applications based on evaluation criteria. This skill performs iterative prompt optimization for LangGraph nodes without changing the graph structure. - Skill: `tomevault-io/fine-tune` (Agent Skill, multi-file: 2 files) - Install (CLI): `npx skillmds@latest add tomevault-io/fine-tune` - Raw SKILL.md: https://api.skillmd.com/api/skills/tomevault-io/fine-tune/raw - Safety review: pending (external: skill-scanner PASS, skillspector PASS) - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: AI & ML - Author: tomevault-io (https://skillmd.com/u/tomevault-io) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/tomevault-io/fine-tune --- # LangGraph Application Fine-Tuning Skill A skill for iteratively optimizing prompts and processing logic in each node of a LangGraph application based on evaluation criteria. ## 📋 Overview This skill executes the following process to improve the performance of existing LangGraph applications: 1. **Load Objectives**: Retrieve optimization goals and evaluation criteria from `.langgraph-master/fine-tune.md` (if this file doesn't exist, help the user create it based on their requirements) 2. **Identify Optimization Targets**: Extract nodes containing LLM prompts using Serena MCP (if Serena MCP is unavailable, investigate the codebase using ls, read, etc.) 3. **Baseline Evaluation**: Measure current performance through multiple runs 4. **Implement Improvements**: Identify the most effective improvement areas and optimize prompts and processing logic 5. **Re-evaluation**: Measure performance after improvements 6. **Iteration**: Repeat steps 4-5 until goals are achieved **Important Constraint**: Only optimize prompts and processing logic within each node without modifying the graph structure (nodes, edges configuration). ## 🎯 When to Use This Skill Use this skill in the following situations: 1. **When performance improvement of existing applications is needed** - Want to improve LLM output quality - Want to improve response speed - Want to reduce error rate 2. **When evaluation criteria are clear** - Optimization goals are defined in `.langgraph-master/fine-tune.md` - Quantitative evaluation methods are established 3. **When improvements through prompt engineering are expected** - Improvements are likely with clearer LLM instructions - Adding few-shot examples would be effective - Output format adjustment is needed ## 📖 Fine-Tuning Workflow Overview ### Phase 1: Preparation and Analysis **Purpose**: Understand optimization targets and current state **Main Steps**: 1. Load objective setting file (`.langgraph-master/fine-tune.md`) 2. Identify optimization targets (Serena MCP or manual code investigation) 3. Create optimization target list (evaluate improvement potential for each node) → See [workflow.md](workflow.md#phase-1-preparation-and-analysis) for details ### Phase 2: Baseline Evaluation **Purpose**: Quantitatively measure current performance **Main Steps**: 4. Prepare evaluation environment (test cases, evaluation scripts) 5. Baseline measurement (recommended: 3-5 runs) 6. Analyze baseline results (identify problems) **Important**: When evaluation programs are needed, create evaluation code in a specific subdirectory (users may specify the directory). → See [workflow.md](workflow.md#phase-2-baseline-evaluation) and [evaluation.md](evaluation.md) for details ### Phase 3: Iterative Improvement **Purpose**: Data-driven incremental improvement **Main Steps**: 7. Prioritization (select the most impactful improvement area) 8. Implement improvements (prompt optimization, parameter tuning) 9. Post-improvement evaluation (re-evaluate under the same conditions) 10. Compare and analyze results (measure improvement effects) 11. Decide whether to continue iteration (repeat until goals are achieved) → See [workflow.md](workflow.md#phase-3-iterative-improvement) and [prompt_optimization.md](prompt_optimization.md) for details ### Phase 4: Completion and Documentation **Purpose**: Record achievements and provide future recommendations **Main Steps**: 12. Create final evaluation report (improvement content, results, recommendations) 13. Code commit and documentation update → See [workflow.md](workflow.md#phase-4-completion-and-documentation) for details ## 🔧 Tools and Technologies Used ### MCP Server Utilization - **Serena MCP**: Codebase analysis and optimization target identification - `find_symbol`: Search for LLM clients - `find_referencing_symbols`: Identify prompt construction locations - `get_symbols_overview`: Understand node structure - **Sequential MCP**: Complex analysis and decision making - Determine improvement priorities - Analyze evaluation results - Plan next actions ### Key Optimization Techniques 1. **Few-Shot Examples**: Accuracy +10-20% 2. **Structured Output Format**: Parsing errors -90% 3. **Temperature/Max Tokens Adjustment**: Cost -20-40% 4. **Model Selection Optimization**: Cost -40-60% 5. **Prompt Caching**: Cost -50-90% (on cache hit) → See [prompt_optimization.md](prompt_optimization.md) for details ## 📚 Related Documentation Detailed guidelines and best practices: - **[workflow.md](workflow.md)** - Fine-tuning workflow details (execution procedures and code examples for each phase) - **[evaluation.md](evaluation.md)** - Evaluation methods and best practices (metric calculation, statistical analysis, test case design) - **[prompt_optimization.md](prompt_optimization.md)** - Prompt optimization techniques (10 practical methods and priorities) - **[examples.md](examples.md)** - Practical examples collection (copy-and-paste ready code examples and template collection) ## ⚠️ Important Notes 1. **Preserve Graph Structure** - Do not add or remove nodes or edges - Do not change data flow between nodes - Maintain state schema 2. **Evaluation Consistency** - Use the same test cases - Measure with the same evaluation metrics - Run multiple times to confirm statistically significant improvements 3. **Cost Management** - Consider evaluation execution costs - Adjust sample size as needed - Be mindful of API rate limits 4. **Version Control** - Git commit each iteration's changes - Maintain rollback-capable state - Record evaluation results ## 🎓 Fine-Tuning Best Practices 1. **Start Small**: Optimize from the most impactful node 2. **Measurement-Driven**: Always perform quantitative evaluation before and after improvements 3. **Incremental Improvement**: Validate one change at a time, not multiple simultaneously 4. **Documentation**: Record reasons and results for each change 5. **Iteration**: Continuously improve until goals are achieved ## 🔗 Reference Links - [LangGraph Official Documentation](https://docs.langchain.com/oss/python/langgraph/overview) - [Prompt Engineering Guide](https://www.promptingguide.ai/) --- > Converted and distributed by [TomeVault](https://tomevault.io/claim/hiroshi75) — claim your Tome and manage your conversions.