🎯 The 3 ways to customize AI (Plain English)

Think of a Large Language Model (LLM) like a new employee. Here’s how you can help them do a specific job:

1) Prompt Engineering (“The Instructions”)

You give the model clear instructions and context right when you ask for something.

• Analogy: Giving an employee a detailed checklist for a single task.
• Pros: Fast, cheap, easy to change.
• Cons: Limited memory (context window); can get tedious to repeat.

2) RAG (Retrieval-Augmented Generation) (“The Library”)

You connect the model to your own documents (PDFs, wiki, database) so it can look up answers before responding.

• Analogy: Giving an employee access to a filing cabinet and saying, “Look it up before you answer.”
• Pros: Accurate facts, up-to-date info, reduces hallucinations, cites sources.
• Cons: Requires setting up a retrieval system (vector DB).

3) Fine-Tuning / DSLMs (“The Training”)

You re-train the model on a specific dataset so it learns a new behavior, style, or vocabulary permanently.

• Analogy: Sending an employee to med school or law school. They internalize the knowledge.
• Pros: Consistent style, format, and jargon without long prompts.
• Cons: Expensive, slow, harder to update (requires re-training), risky for facts (can hallucinate confident nonsense).

⚡ The Comparison Table (At a Glance)

🧭 The Decision Framework: Which one do you need?

Use this simple logic to decide.

Scenario A: “I need it to know facts about my company/products.”

Answer: Use RAG.
Do not fine-tune for facts. Models are bad at memorizing facts accurately and efficiently. RAG lets the model “read” the right fact instantly. Plus, when pricing changes, you update one document—you don’t re-train a neural network.

Scenario B: “I need it to speak in our specific brand voice or code style.”

Answer: Try Fine-Tuning (eventually).
If you have thousands of examples of “good output” and prompts aren’t quite getting the style right, fine-tuning helps the model internalize that vibe.

Scenario C: “I need both.”

Answer: Hybrid (RAG + Fine-Tuning).
Fine-tune a model to understand your industry jargon (e.g., medical or legal terms), then use RAG to fetch the specific patient record or case file.

⚠️ The “Prompt First” Golden Rule

Before you build a RAG system or fine-tune a model, always start with Prompt Engineering.

Many “model problems” are actually just “bad instruction problems.” Try giving the model examples (Few-Shot Prompting) first. Only build RAG if prompts aren’t enough. Only fine-tune if RAG isn’t enough.

🧪 Mini-lab: Test it yourself

The Task: You want an AI to answer questions about your company’s “Work From Home Policy.”

1. Prompt Test: Paste the policy text into ChatGPT and ask a question. (It works perfectly, but is tedious to paste every time).
2. RAG Test: Imagine uploading that PDF to a “knowledge base.” The AI searches it automatically. (Perfect for scale).
3. Fine-Tuning Test: Imagine training a model on the policy. Next month, the policy changes. The model still “remembers” the old rule. (This is why fine-tuning is bad for facts).

🔗 Keep exploring on AI Buzz

• Retrieval Augmented Generation (RAG) Explained
• Domain-Specific Language Models (DSLMs)
• Secure RAG: Risks & Guardrails
• AI-SBOM: Documenting Your System

🏁 Conclusion

Don’t overcomplicate it.

• Need facts? Use RAG.
• Need style? Use Fine-Tuning.
• Just starting? Use Prompts.

Choosing the right architecture saves you money, reduces hallucinations, and makes your AI system actually useful.