LLM Fine-tuning

Next Layer: Fine-Tuning

Where RAG retrieves knowledge dynamically, fine-tuning actually modifies the model’s brain — it teaches the LLM new patterns or behaviors by updating its internal weights.

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⚙️ How Fine-Tuning Works

1. Start with a pretrained model (e.g., GPT-3.5, Llama-3, Mistral).

2. Prepare training data — examples of how you want the model to behave:

   • Inputs → desired outputs

   • e.g., “User story → corresponding UAT test case”

3. Train the model on these examples (using supervised learning or reinforcement learning).

4. The model’s weights are adjusted, internalizing the new style, tone, or domain language.

After fine-tuning, the model natively performs the desired task without needing the examples fed each time.

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⚖️ RAG vs Fine-Tuning: Clear Comparison

Aspect	RAG (Retrieval-Augmented Generation)	Fine-Tuning
Mechanism	Adds external info at runtime	Alters model weights via training
When Used	When data changes often or is large	When you need consistent behavior or reasoning style
Data Type	Documents, databases, APIs	Labeled prompt–response pairs
Cost	Low (no retraining)	High (GPU time, expertise, re-training)
Freshness	Instantly updatable	Requires re-training to update
Control	You control retrieved sources	You control reasoning patterns
Example Use	Ask questions about new policies	Teach model to write test cases in your company’s format
Analogy	Reading from a manual before answering	Rewriting the brain to remember the manual forever

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🧩 Combining Both: RAG + Fine-Tuning = Domain-Native AI

The real power comes when both are used together:

Layer	Role
Fine-Tuning	Teaches the model how to think — e.g., how to structure a UAT test case, how to handle defects, your tone/style.
RAG	Gives it the latest knowledge — e.g., current epics, Jira stories, or Salesforce objects from your live data.

So the LLM becomes:

A fine-tuned specialist with a live retrieval memory.

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🧬 Example: In Your AGL Salesforce / UAT Context

Step	Example
Fine-tuning	You fine-tune the LLM on 1,000 existing UAT test cases and business rules. Now it understands your structure and tone.
RAG layer	You connect it to Jira and Confluence via embeddings, so when you ask, “Generate UAT test cases for Drop-3 Call Centre Epics,” it retrieves the latest epics and acceptance criteria.
Result	You get context-aware, properly formatted, accurate UAT cases consistent with AGL’s standards.

That’s enterprise-grade augmentation — the model both knows how to think like your testers and knows what’s new from your systems.

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🧠 Summary Table

Capability	Base LLM	+ RAG	+ Fine-Tuning	+ Both
General reasoning	✅	✅	✅	✅
Access to private or new data	❌	✅	⚠ (only if baked in)	✅
Domain vocabulary & formats	⚠	⚠	✅	✅
Updatable knowledge	❌	✅	❌	✅
Low hallucination	⚠	✅	✅	✅✅
Cost to build	–	Low	Medium–High	Medium

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🚀 The Strategic Rule of Thumb

If your problem is...	Then use...
“Model doesn’t know the latest information.”	✅ RAG
“Model doesn’t behave or write like us.”	✅ Fine-Tuning
“Model doesn’t know and doesn’t behave correctly.”	✅ Both

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That’s the progressive architecture:

• RAG extends knowledge.

• Fine-tuning embeds behavior.

• Together, they form the foundation for enterprise-grade AI systems.