AI
RAG vs Fine-Tuning: Which Should You Use to Add Your Data to an LLM?
Teams often reach for fine-tuning when they actually need retrieval. This guide explains the difference and how to decide without guesswork.
By Prolificrew Engineering · · 4 min read
In this article
A general-purpose language model knows a great deal about the world but nothing about your company’s documents, products or customers. There are two main ways to fix that: retrieval-augmented generation (RAG), which gives the model relevant information at the moment of the question, and fine-tuning, which changes the model itself by training it on examples. They solve different problems, and confusing them is one of the most common and costly mistakes in AI projects.
Rule of thumb: use RAG to teach the model what to know, and fine-tuning to teach it how to behave.
How RAG works
- Ingest. Your documents are collected and split into chunks of a sensible size.
- Embed. Each chunk is converted into a numeric vector that captures its meaning.
- Store. The vectors go into a search index or vector database, together with the original text and metadata such as source and permissions.
- Retrieve. When a user asks a question, the question is embedded and the most similar chunks are found. Many systems combine this with keyword search and a re-ranking step.
- Generate. The retrieved chunks are placed in the prompt and the model answers using them, ideally citing the sources.
Because knowledge lives in the index and not in the model, updating it is as simple as re-indexing a document. You can also restrict who can see what by filtering on permissions at retrieval time.
How fine-tuning works
Fine-tuning continues the model’s training on a curated set of example inputs and desired outputs. Done well, it teaches a consistent style, a strict output format, domain-specific phrasing or a specialised task, such as classifying support tickets into your categories. It is not a reliable way to store facts: models can still forget, blur or invent details that were in the training data.
Side-by-side comparison
| RAG | Fine-tuning | |
|---|---|---|
| Best for | Answers grounded in your documents and data | Consistent style, format or specialised behaviour |
| Knowledge updates | Immediate: re-index the document | Requires new training runs |
| Cites sources | Yes, by design | No |
| Access control | Possible at retrieval time | Not possible: the knowledge is in the weights |
| Upfront effort | Data pipeline and search quality | High-quality example dataset and training |
| Typical failure | Retrieves the wrong passage | Confidently reproduces patterns that are out of date or wrong |
| Running cost | Longer prompts and a search index | Hosting or using a customised model |
When RAG is the right first step
- Support assistants answering from help-centre articles and policies.
- Internal search over contracts, wikis, tickets or research.
- Anything where the answer changes often or must point to a source.
- Products with per-customer data that must stay separated.
When fine-tuning earns its place
- You need a strict output structure every time, and prompting alone is unreliable.
- You have a narrow, repeatable task with plenty of labelled examples (classification, extraction, routing).
- You want a smaller, cheaper model to match a larger one on a specific task.
- You need a distinctive tone of voice applied consistently.
Many production systems use both: RAG supplies the facts and a fine-tuned or carefully prompted model shapes the response. Start with RAG and good prompting. Move to fine-tuning only when you can show, with measurements, that prompting and retrieval have hit a ceiling.
Where RAG projects actually fail
Teams usually blame the model when answers are wrong. More often the cause is retrieval: documents were split in the middle of a thought, tables and PDFs were extracted badly, old versions of a policy are still indexed, or the question is phrased so differently from the document that similarity search misses it. Improving chunking, adding metadata filters, combining keyword and vector search and re-ranking results usually helps more than changing the model.
Evaluate before you launch
Whatever you choose, build an evaluation set first: a list of real questions with the correct answers and the source documents that support them. Then measure, every time you change something:
- Retrieval quality: did the right passage appear in the top results?
- Faithfulness: is every claim in the answer supported by the retrieved text?
- Answer quality: is it complete, correct and appropriately worded?
- Cost and latency: tokens per answer and time to first word.
Guardrails and honest limits
RAG reduces hallucinations; it does not eliminate them. Design the product with that in mind: show sources next to answers, let the model say it does not know when nothing relevant is retrieved, log questions and answers for review, check inputs and outputs for sensitive data, and keep a human in the loop for high-stakes decisions. Be clear with users about what the assistant can and cannot do.
If you are planning an AI feature, our AI integration team can help you choose the architecture, build the evaluation set and take it to production. You can also contact us to discuss your use case.