The Core Question
You want an AI that knows your company's data. You have two main options:
- Fine-tuning — Retrain the AI model on your data so it "memorizes" it
- RAG — Give the AI your documents at query time so it can look up answers
Both work. But they're right for very different situations.
What Is Fine-Tuning?
Fine-tuning takes a pre-trained model (like GPT-4) and continues training it on your specific dataset. The model's weights are updated to incorporate your information.
Think of it like: Sending an employee to a 6-month training program where they study your company inside and out.
The result: A model that has internalized your style, terminology, and domain knowledge.
What Is RAG?
RAG (Retrieval-Augmented Generation) keeps the base model unchanged. Instead, when a user asks a question, relevant passages from your documents are retrieved and handed to the model as context.
Think of it like: Giving an employee access to a well-organized filing cabinet. They don't memorize everything — they look up what they need.
The result: A model that can answer questions based on your documents, with answers grounded in retrievable sources.
When to Use Fine-Tuning
Fine-tuning makes sense when:
- You need the model to adopt a specific writing style or tone
- You're working with a specialized domain where the base model lacks vocabulary (medical, legal, highly technical)
- Your data is stable and doesn't change often
- You have a large, high-quality training dataset
Downsides of fine-tuning:
- Expensive — requires significant compute and ML expertise
- Slow to update — every time your data changes, you retrain
- No source attribution — the model can't tell you where it got an answer
When to Use RAG
RAG makes sense when:
- Your data changes frequently (policies, product updates, pricing)
- You need source attribution — users want to know where the answer came from
- You want to get started quickly and affordably
- You're building a document Q&A or customer support use case
Downsides of RAG:
- Quality depends on retrieval quality — bad search = bad answers
- Slightly higher latency (retrieval step adds time)
- Context window limits how much information can be passed to the model
The Honest Answer for Most Businesses
For the vast majority of business use cases — customer support, internal knowledge bases, document Q&A — RAG is the right choice.
It's faster to implement, cheaper to run, easier to update, and gives you source citations. Fine-tuning is powerful but overkill for most scenarios.
How Chatleon Implements RAG
Chatleon uses a multi-step RAG pipeline:
- Your documents are chunked and embedded using OpenAI's embedding model
- Embeddings are stored in a vector database (pgvector)
- When a user asks a question, the most relevant chunks are retrieved
- If you've enabled it, Cohere's reranker improves retrieval quality
- The retrieved chunks are passed to GPT-4o-mini, which generates the answer
The result: accurate, source-grounded answers from your documents — without retraining anything.