Fine-Tuning vs Embeddings: Which Training Method Should I Use?
AIWU supports two ways to teach AI about your business: embeddings and fine-tuning. They sound similar but work completely differently — and choosing the wrong one wastes time and money. This guide explains the difference with real examples and tells you exactly which to use.
Before You Start
You’ll need:
- AIWU plugin installed with an API key configured (Choose your AI provider)
- Time needed: ~5 minutes to read and decide
- Plan required: Pro (for AI Training features)
The Short Answer
| Embeddings | Fine-Tuning | |
|---|---|---|
| What it does | Gives the AI access to your documents to look up answers | Permanently changes how the AI writes and thinks |
| Best for | FAQs, product info, policies, knowledge bases | Specific writing style, tone, specialized vocabulary |
| Setup time | 10–30 minutes | Several hours + iterations |
| Updates | Easy — add/edit documents anytime | Hard — must retrain to change anything |
| Cost | Low (embedding queries are cheap) | High (training runs cost significantly more) |
| Recommended for most users | ✅ Yes | Only for specific use cases |
What Are Embeddings?
Embeddings work like giving your AI a searchable filing cabinet full of your business documents. When a visitor asks a question, the AI searches the cabinet, finds the relevant information, and uses it to answer.
Example: You upload your product catalog (200 products with descriptions, prices, specs). A visitor asks “Do you have hiking boots under $80?” — the AI searches the catalog, finds matching products, and responds with accurate details.
The AI doesn’t “learn” permanently — it reads your documents in real time. This means you can update your documents anytime and the AI immediately uses the new information.
Use embeddings when:
- You want the chatbot to answer questions about your products, services, or policies
- Your information changes (prices update, new products added)
- You have structured data: FAQs, product lists, support docs, pricing tables
- You want to get started quickly
What Is Fine-Tuning?
Fine-tuning modifies the AI model itself by training it on examples of the writing style and domain knowledge you want it to have. The AI permanently “absorbs” patterns from your examples.
Example: You’re a legal tech company that wants the AI to write exactly like your firm — formal Latin-derived phrasing, specific citation formats, a particular structure for legal summaries. You feed it 500 examples of correctly formatted legal documents. After training, every output naturally matches that style — without you prompting for it.
Fine-tuning does NOT work well for:
- Teaching the AI specific facts (it hallucinates facts even after fine-tuning — use embeddings for facts)
- Keeping up with changing information (you’d have to retrain)
- Small datasets (you need hundreds of quality examples)
Use fine-tuning when:
- You need a highly specific writing style the AI doesn’t naturally produce
- You work in a specialized domain with unique vocabulary (legal, medical, technical)
- Consistency of output format is more important than factual accuracy
- You have 200+ high-quality training examples
Real-World Decision Examples
| Situation | Right choice | Why |
|---|---|---|
| Online store chatbot that answers product questions | Embeddings | Products change; the AI needs to look up current catalog |
| Support chatbot trained on your FAQ and policies | Embeddings | Facts need to be accurate and up to date |
| Content generator that writes in your specific brand voice | Fine-tuning | Style consistency matters more than dynamic information |
| Medical practice chatbot answering symptom questions | Embeddings + careful prompting | Facts must come from your approved documents |
| AI that writes legal briefs in your firm’s exact format | Fine-tuning | Highly specific structural format, specialized vocabulary |
| Real estate agency chatbot that knows your listings | Embeddings | Listings change constantly; embeddings update instantly |
Can I Use Both?
Yes — and for advanced setups, combining both works well. Fine-tune for writing style + use embeddings for factual knowledge. Example: fine-tune the AI to always write in your brand voice, then give it embeddings access to your product catalog so it uses that voice when describing accurate product details.
What’s Next
- 🧠 Ready to start with embeddings (recommended): Embeddings in 10 Minutes: Make Your AI Know Your Business
- 🔬 Want to explore fine-tuning: Training Data Best Practices: What Makes a Good Dataset
- 💬 Apply training to your chatbot: Train Your Chatbot with Embeddings
Last verified: AIWU v.4.9.2 · Updated: 2026-02-25