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Series · Part 12 of 21

Practice
AI Demystified
Abhishek Saha
Abhishek Saha · 🤖 AI / ML

Fine-tuning vs. Prompting — When to Use Which

Prompt engineering gets you 80% of the way. Fine-tuning gets you the last 20%. Here's a clear decision framework for choosing between them.

#ai #fine-tuning #prompt-engineering #llm #machine-learning
Fine-tuning vs. Prompting — When to Use Which

You’ve been prompting for weeks. It’s 80% there. Should you fine-tune? Here’s a visual decision framework for when prompting is enough, when RAG is better, and when fine-tuning actually makes sense.

FINE-TUNING vs RAG vs PROMPTING— choosing your approach

Three ways to change how an AI behaves. Each adjusts a different "knob" — and each comes with trade-offs.

✍️Prompt Engineering
CHANGES
What you say to the model
WHEN
When you need to steer style, tone, or format without changing the model
EFFORTLow
📚RAG
CHANGES
What knowledge the model can access
WHEN
When the model needs facts, documents, or data it wasn't trained on
EFFORTMedium
🔧Fine-Tuning
CHANGES
The model's weights themselves
WHEN
When you need reliable output format or consistent behavior
EFFORTHigh
Rule of thumb: Start with prompting (free, instant). Add RAG when you need facts (moderate effort). Fine-tune only when you need consistent behavior that can't be achieved otherwise (high effort).

Next up: Part 13 continues the Practice track — model selection. You’ve learned how to use AI effectively; now learn how to choose the right model for the job (it’s often not the biggest one).

AI Demystified · 16 of 21 published

  1. 0 Grounding 5 Mental Models You Need Before Diving Into AI
  2. 1 Foundation What Happens When You Ask AI Something?
  3. 2 Foundation Transformers — The Architecture That Changed Everything
  4. 3 Foundation How AI Learns, Thinks, and Decides
  5. 4 Foundation How AI Reads Your Words
  6. 5 Foundation Why AI Forgets
  7. 6 Foundation Why AI Lies (And Doesn't Know It)
  8. 7 Foundation What AI Cannot Do
  9. 8 Foundation How AI Reasons (And Why It Sometimes Breaks)
  10. 9 Practice Prompt Engineering — How to Talk to AI
  11. 10 Practice Embeddings & Vector Databases — The Memory Layer of AI
  12. 11 Practice RAG Explained — How AI Knows What You Didn't Train It On
  13. 12 Practice Fine-tuning vs. Prompting — When to Use Which
  14. 13 Practice Do You Really Need GPT-4?
  15. 14 Practice Latency, Tokens, and Cost — The Physics of AI Products
  16. 15 Practice How Do You Know AI Is Actually Working?
  17. 16 Hands-On Coding Setup — Your AI Development Environment soon
  18. 17 Hands-On MCP Tool Calling — How AI Uses Tools soon
  19. 18 Hands-On AI Agents — Beyond Chatbots soon
  20. 19 Hands-On Build Your First Real AI App soon
  21. 20 Hands-On Token Optimization — Spend Less, Get More soon
← Part 11 RAG Explained — How AI Knows What You Didn't Train It On Part 13 → Do You Really Need GPT-4?
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