The Customization Question
You want an AI system that knows about your business — your products, your processes, your data. There are two primary approaches: Retrieval-Augmented Generation (RAG) and fine-tuning. Choosing wrong can cost you months of work and thousands of dollars.
RAG: Retrieve Then Generate
RAG works by giving the AI model access to your documents at query time. When a user asks a question, the system searches your knowledge base, retrieves relevant passages, and includes them in the prompt context.
Pros:
- Quick to set up (days, not weeks)
- No model training required
- Data stays in your control
- Easy to update — just add or remove documents
- Works with any base model
Cons:
- Quality depends on your retrieval system
- Context window limits how much information you can include
- Can be slower due to the retrieval step
- May struggle with questions that require synthesizing many documents
Best for: Internal knowledge bases, customer support, document Q&A, policy lookup — any case where the answer exists in your documents.
Fine-Tuning: Train the Model
Fine-tuning adjusts the model's weights using your specific data. The model learns your domain's patterns, terminology, and style.
Pros:
- Can learn domain-specific patterns and terminology
- Faster inference (no retrieval step)
- Can handle nuanced domain knowledge
Cons:
- Requires significant training data
- Expensive and time-consuming
- Model needs retraining when information changes
- Risk of overfitting or degrading general capabilities
- More complex to maintain
Best for: Specific writing styles, domain-specific classification, tasks where the pattern is consistent and the data is static.
Our Recommendation
Start with RAG. For 80% of business use cases, RAG delivers better results with less effort and cost. You can have a working prototype in days instead of weeks.
Consider fine-tuning only when:
- RAG isn't capturing the nuance you need
- You have thousands of high-quality training examples
- The use case justifies the ongoing maintenance cost
- Your data doesn't change frequently
Most of our engagements start with RAG and never need fine-tuning. The ones that do move to fine-tuning do so with clear evidence that RAG isn't sufficient.