Why you need this
You need better AI outputs. But should you:
- Write better prompts?
- Build a RAG system?
- Fine-tune a model?
This decision tree gives you the answer in 60 seconds.
Perfect for:
- Developers deciding how to customize AI models
- Product managers scoping AI features
- CTOs evaluating technical approaches
- Teams trying to avoid over-engineering (or under-engineering)
What's inside
The Decision Tree
Answer 5 simple questions:
Do you need the AI to know specific facts or data?
- Yes → Consider RAG (Retrieval-Augmented Generation)
- No → Continue
Is your data public or commonly known?
- Yes → Use better prompting
- No → Consider RAG or fine-tuning
Do you need a specific writing style, tone, or format?
- Yes → Consider fine-tuning
- No → Use better prompting
Is cost a major concern? (Fine-tuning = $$$)
- Yes → Try prompting or RAG first
- No → Fine-tuning is an option
How often does your data change?
- Constantly → Use RAG (easy to update)
- Rarely → Fine-tuning might work
The Three Paths
Path 1: Better Prompting
- When: You need general knowledge or creative tasks
- Cost: Free to cheap
- Effort: Low (minutes to hours)
- Examples: Drafting emails, brainstorming, summarizing
Path 2: RAG (Retrieval-Augmented Generation)
- When: You need AI to access your own data (docs, databases, FAQs)
- Cost: Moderate (vector DB + API calls)
- Effort: Medium (days to weeks)
- Examples: Customer support bots, document Q&A, internal knowledge bases
Path 3: Fine-tuning
- When: You need a specific style, tone, or highly specialized behavior
- Cost: High (training + infrastructure)
- Effort: High (weeks to months)
- Examples: Medical diagnosis, legal document generation, brand-specific writing
Cost & Complexity Comparison Table
| Approach | Cost | Effort | Update Speed | Use Case |
|---|---|---|---|---|
| Prompting | $ | Hours | Instant | General tasks |
| RAG | $$ | Days | Fast (just update docs) | Custom data access |
| Fine-tuning | $$$ | Weeks | Slow (requires retraining) | Specialized behavior |
How to use it
- Start here before committing to a technical approach
- Save time and money by avoiding unnecessary fine-tuning
- Share with stakeholders to explain technical trade-offs
- Revisit quarterly — as your needs evolve, your approach might change
Real-world examples
Example 1: E-commerce Chatbot
- Need: Answer product questions from your catalog
- Decision: RAG (constantly updating inventory)
- Why not fine-tuning? Too slow to retrain every time products change
Example 2: Legal Document Generator
- Need: Generate contracts in your firm's exact style and format
- Decision: Fine-tuning (specific style + compliance requirements)
- Why not RAG? Style and tone are more important than facts
Example 3: Marketing Copy
- Need: Draft social media posts and blog intros
- Decision: Better prompting (general task, doesn't require custom data)
- Why not fine-tuning? Overkill for a simple creative task
Want to go deeper?
This decision tree is your starting point. For implementation guides and advanced techniques:
- Embeddings & RAG Guide — How to build a RAG system
- Prompt Engineering Basics — Master the easiest path first
- Glossary: Fine-tuning — Understanding model customization
License & Attribution
This resource is licensed under Creative Commons Attribution 4.0 (CC-BY). You're free to:
- Share with your engineering team
- Print for architecture planning sessions
- Include in technical proposals
Just include this attribution:
"Fine-tuning Decision Tree" by Field Guide to AI (fieldguidetoai.com) is licensed under CC BY 4.0
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