Can fine-tuning teach an LLM new facts?

Fine-tuning can nudge a model toward certain facts, but it is an unreliable way to store knowledge. The model may still mix facts up, cannot cite them and needs retraining whenever they change. RAG is the standard tool for knowledge.

Is RAG cheaper than fine-tuning?

RAG usually costs less to start because there is no training job, but every request carries extra context tokens. Fine-tuning has an upfront training cost and ongoing retraining cost, but prompts can be shorter. The real cost depends on traffic and how often data changes.

Should I try prompt engineering before either one?

Yes. A clear system prompt with a few examples often solves style and format problems without fine-tuning. Add RAG when the model lacks knowledge, and fine-tune only when prompting has clearly hit its limit.

Does RAG work with open models like Llama?

Yes. RAG is model-agnostic. The retrieval step is separate from the LLM, so the same index can feed OpenAI models, Claude or a self-hosted Llama model.

RAG vs fine-tuning: which one does your AI app need?

What is the short answer?

Choose retrieval-augmented generation (RAG) when your AI app needs to know things, and choose fine-tuning when it needs to behave a certain way. RAG feeds the model relevant documents at question time, so answers stay current and can cite sources. Fine-tuning trains the model on examples, so it reliably follows a style, format or narrow task.

For most business chatbots, the right order is: prompt engineering first, then RAG, then fine-tuning only if a clear gap remains. Many production apps never need fine-tuning at all.

What is the difference between RAG and fine-tuning?

RAG leaves the model unchanged. At every request, the app searches a document index for the passages most relevant to the question and adds them to the prompt. The model then answers from that context. The article What is RAG? explains the full pipeline.

Fine-tuning changes the model itself. You train a base model on many example conversations, and its weights are updated so it imitates those examples. After fine-tuning, the model follows the learned patterns without being told each time.

A useful way to remember the difference: RAG is like giving someone an open book during an exam. Fine-tuning is like sending them to a training course before the exam. The open book helps with facts; the course helps with habits.

How do RAG and fine-tuning compare?

The table below compares the two approaches on the points that usually decide the choice.

Factor	RAG	Fine-tuning
Freshness of knowledge	Update the index and answers change right away	Needs a new training run to change
Cost to start	Low: embed documents, no training job	Higher: prepare data and run training
Cost per request	More input tokens for retrieved context	Shorter prompts are possible
Citations	Yes, each chunk has a known source	No, knowledge is mixed into the weights
Style and output format	Controlled by the prompt, can drift	Strong and consistent once trained
Data needed	Your documents, cleaned and chunked	Many high-quality input and output examples
Hallucination control	Answers can be checked against sources	Harder to verify where an answer came from
Maintenance	Keep the index in sync with the source data	Retrain when behavior or data needs change
Model choice	Works with almost any LLM	Tied to a model that supports fine-tuning

The table shows why RAG is usually the default for knowledge-heavy apps. Freshness and citations are hard requirements for support bots, internal search and document Q&A, and fine-tuning cannot provide them.

When should you use RAG?

RAG is the right choice when the answer lives in documents you control. Typical cases include customer support over help articles, internal knowledge bases, contract and policy Q&A, and product catalogs. In all of these, the facts change and users need to trust the source.

RAG also fits when you have many small knowledge sets. A multi-tenant chatbot platform can keep one index per customer and use the same model for everyone. In the no-code AI bot framework I built, each bot used RAG through LlamaIndex over its own data sources, on top of a configurable LLM such as OpenAI or Llama 3.

RAG is also the safer choice when you need to show your work. Because every retrieved chunk carries metadata, the app can list the files or pages used for an answer.

When should you use fine-tuning?

Fine-tuning is the right choice when the problem is behavior, not knowledge. Good cases include a fixed output format such as strict JSON, a brand voice that must stay consistent, classification or extraction tasks with clear labels, and domain wording that the base model keeps getting wrong.

Fine-tuning also helps when prompts grow too long. If a system prompt needs dozens of examples to keep the model on track, training those examples into the model can shorten prompts and make results steadier.

Fine-tuning needs good training data. Most fine-tuning services accept chat-style examples in JSON Lines (JSONL) format, one conversation per line:

{"messages": [{"role": "system", "content": "You classify support tickets."}, {"role": "user", "content": "My card was charged twice."}, {"role": "assistant", "content": "{\"category\": \"billing\", \"urgency\": \"high\"}"}]}
{"messages": [{"role": "system", "content": "You classify support tickets."}, {"role": "user", "content": "How do I change my email?"}, {"role": "assistant", "content": "{\"category\": \"account\", \"urgency\": \"low\"}"}]}

Each example should show the exact output you want. Inconsistent or low-quality examples teach the model inconsistent behavior.

When should you combine RAG and fine-tuning?

Combine the two when you need both reliable behavior and fresh knowledge. Fine-tune the model for tone, format and task habits, and use RAG to supply the facts at question time. The fine-tuned model then becomes better at using retrieved context in the way you want.

A common combined setup is a support assistant that must answer from current help articles and always reply in a fixed structure, such as a short answer, steps and a source link. RAG provides the articles; fine-tuning locks in the structure.

Only combine them after each part is proven. Start with RAG and a strong prompt, measure the gaps on real conversations, and fine-tune only for gaps that prompting cannot close. The prompt engineering checklist covers what to try before training anything.

How do you decide for your own app?

A few questions settle the choice for most projects.

Does the answer depend on documents or data that change? Use RAG.
Do users need to see sources? Use RAG.
Is the main problem tone, format or a narrow repeated task? Try prompting first, then fine-tuning.
Do you have hundreds of clean, consistent examples of ideal outputs? If not, fine-tuning is not ready yet.
Do you need both current facts and strict behavior? Combine them, starting with RAG.

Summary

RAG adds knowledge at question time and fine-tuning shapes behavior through training. Use RAG for changing facts and citations, fine-tuning for consistent style and narrow tasks, and both when you need both. Start with prompts and RAG, measure, and fine-tune only for a proven gap. For help choosing and building the right setup, see the AI applications service.