FINE_TUNING_VS_RAG.md

🧠 Fine-Tuning vs RAG: When to Use Each

Overview

Choosing between RAG (Retrieval-Augmented Generation) and fine-tuning depends on your use case, data type, infrastructure, and latency/accuracy needs. This guide helps you understand when to use each technique.

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🧠 When to Use Fine-Tuning

You change the model itself.

✅ Best for:

• Specialized tone, domain, or logic: e.g., legal, medical, or customer service with unique jargon or response styles.
• Consistent and controlled outputs: e.g., structured responses, classification, or generation following strict formats.
• Offline or edge usage: where external lookups (APIs/dbs) aren't possible.
• Low-latency, high-volume applications: inference is faster after fine-tuning (vs retrieving and reranking documents).

❗️Tradeoffs:

• Computationally expensive (GPU-intensive).
• Needs retraining for updates (not dynamic).
• Risk of catastrophic forgetting if not done properly.

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🔍 When to Use RAG (Retrieval-Augmented Generation)

You keep the model static, and inject external knowledge at inference time.

✅ Best for:

• Dynamic knowledge: e.g., FAQs, product info, or fast-changing data (support bots, search).
• Long context retrieval: handle large corpora without needing it "baked" into the model.
• Fewer resources: no need to retrain models, just update the database or vector index.
• Explainability: you can cite sources (good for legal/medical use cases).

❗️Tradeoffs:

• Higher latency (retrieval + generation).
• Needs a solid retrieval pipeline (embedding model, vector store, reranking).
• Limited by context window (injectable content).

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⚔️ RAG vs Fine-Tuning: Quick Comparison

Feature	Fine-Tuning	RAG
Knowledge Update	Static (retrain)	Dynamic (just update data)
Accuracy on niche data	✅ High	⚠️ Medium
Resource cost	High (GPUs needed)	Low-Medium (CPU OK)
Explainability	❌ Low	✅ High
Development time	Longer	Faster
Model behavior change	Permanent	Temporary (context-based)

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🚀 Real-World Examples

Use Case	RAG	Fine-Tune
Legal search assistant	✅	⚠️ (if tone is needed)
Support chatbot	✅	✅ (if tone/flow matter)
Medical diagnosis	⚠️	✅
Resume/CV fixer	✅	✅
Programming Q&A bot	✅	❌

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Summary

Use fine-tuning when you want to deeply specialize a model's behavior, tone, or logic. Use RAG when you need flexible, dynamic access to external knowledge with less overhead. In many cases, combining both can yield the best results.