Fine-tuning vs. RAG

Fine-tuning involves modifying the internal weights of an LLM by training it on new data. This process is computationally expensive but results in a more deeply adapted model.

🔍 Fine-tuning

• Definition: Training the model on a new dataset to adjust its behavior permanently.

• Types:

  • Supervised fine-tuning (SFT): Learn from labeled examples.

  • Instruction-tuning: Learn from instructions and demonstrations.

  • Reinforcement learning: Optimize output quality through feedback loops.

✅ Advantages:

• Produces highly tailored behavior and tone.

• No external retrieval needed.

• Lower latency at inference time.

⚠️ Disadvantages:

• Costly (compute + time).

• Static: doesn't adapt to real-time data.

• Hard to audit or update knowledge.

🔁 RAG

• Definition: Uses an external retriever to supply relevant context before generation.

• Behavior: Instead of “teaching” the model, RAG lets it “read” external documents live.

✅ Advantages:

• Real-time adaptability.

• Transparent (can show sources).

• Cheaper and faster to update (no retraining).

⚠️ Disadvantages:

• Slightly slower due to retrieval step.

• Needs well-maintained document storage (corpus + vector DB).

• More moving parts (retrievers, rerankers, etc.).