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Should I use fine-tuning or RAG?

Quick Answer

Use RAG when you need the AI to answer questions using your specific documents and data. Use fine-tuning when you need to change the model's behaviour, tone, or output format. For most business applications, RAG is the better starting point: it is faster to implement, easier to update, provides source attribution, and does not require expensive model training. Fine-tuning complements RAG for specialised needs.

Summary

Key takeaways

  • RAG is best for grounding AI in your specific knowledge and documents
  • Fine-tuning is best for changing model behaviour, tone, or output format
  • RAG is faster and cheaper to implement and maintain for most use cases
  • Many production systems combine both approaches for optimal results

When to Use RAG vs Fine-Tuning

RAG is the right choice when your primary goal is to make an AI system knowledgeable about your organisation's specific information. It excels at question-answering over documents, knowledge base searches, and any application where you need the AI to cite its sources. RAG is also appropriate when your data changes frequently, as updates are immediate without retraining. Fine-tuning is the right choice when you need to alter the fundamental behaviour of a model: teaching it a specific writing style, making it follow complex formatting rules consistently, or training it to handle domain-specific language and jargon. Fine-tuning is also valuable when you need consistently structured outputs, such as always producing JSON in a specific schema or generating reports in a particular format.

Practical Comparison

From a practical standpoint, RAG is significantly faster to implement, typically 2 to 6 weeks versus 4 to 12 weeks for fine-tuning. RAG costs less upfront because it uses existing models through APIs rather than training custom models. RAG provides natural source attribution, showing users which documents informed the answer. Updating RAG is simple: add, modify, or remove documents from the index. Fine-tuning requires collecting training data, running the training process, evaluating results, and iterating, which is more resource-intensive. However, fine-tuned models can be more efficient at inference time since the knowledge is embedded in the model weights rather than requiring a retrieval step. For high-volume, latency-sensitive applications, this efficiency can be significant.

Related

Related questions

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FAQ

Frequently asked questions

Yes, and many production systems do. You might fine-tune a model to follow your organisation's communication style while using RAG to ground its responses in your specific documents. This combination delivers both behavioural consistency and factual accuracy.

Not usually. Modern large language models already understand most industry terminology. RAG with well-organised domain documents typically outperforms fine-tuning for domain-specific question-answering. Fine-tuning adds value for specialised output formats or very niche terminology.

RAG is generally more effective at reducing hallucinations because it provides explicit source material for the model to reference. Fine-tuning can reduce certain types of errors but does not eliminate hallucinations as reliably as RAG with proper source grounding.

RAG implementation typically costs £10,000 to £50,000 with minimal ongoing costs. Fine-tuning costs £5,000 to £50,000 per training run, with additional costs for data preparation, evaluation, and retraining as requirements change. RAG is generally more cost-effective for most business applications.

Yes, and this is the recommended approach. RAG delivers value quickly while you gather data and identify specific areas where fine-tuning would add incremental improvement. Many organisations find RAG alone meets their needs without ever needing fine-tuning.

Related Content

RAG Implementation Guide

How to build a RAG system

Fine-Tuning Guide

When and how to fine-tune language models

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