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Use RAG when an LLM needs current, private, or source-backed information. Use fine-tuning when it has the information but applies a task, format, or style inconsistently. Start with a prompt-only baseline and evaluation; add the method that addresses the measured failure. Combine them only if testing shows you need both information access and more consistent behavior.
What problem are you trying to fix?
RAG and fine-tuning solve different problems. In retrieval-augmented generation, the system fetches relevant material from an external source and includes it in the model’s context. Fine-tuning updates model parameters using examples. Neither is automatically the better choice: the right one depends on the task and results from representative evaluation. OpenAI’s optimization guide and AWS’s comparison describe them as different approaches to different needs.
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| Observed need | First approach to evaluate | Why, and what to check |
|---|---|---|
| Answers depend on current or frequently changing internal documents | RAG | Update the retrieval corpus instead of retraining just to reflect changed information. Check whether retrieval finds the right passages. |
| Answers should identify their supporting documents | RAG | Retrieved sources can be exposed as references, but the system still needs to retrieve relevant evidence and construct accurate citations. |
| Task, format, or style is inconsistent even when the model has useful context | Fine-tuning, after a prompt and evaluation baseline | Examples can teach recurring behavior. Training examples should resemble production inputs, and evaluation should include held-out cases. |
| One occasional question about one document | In-context prompting | Passing the document in the request may be enough. AWS describes this as suitable for ad hoc querying of a single document, rather than many documents or enterprise systems. |
| Both changing/private knowledge and consistent task behavior are needed | Evaluate RAG plus fine-tuning | The methods can be combined, but added retrieval noise and maintenance can hurt. Compare the combination with each single-method baseline. |
| No evaluation shows that either method is necessary | Improve the prompt and baseline first | OpenAI advises improving accuracy with simpler methods before adding system complexity. |
How RAG works—and what it does not do
A typical RAG system uses a question to retrieve likely relevant passages from an external knowledge base, places those passages into the model’s context, and asks the model to answer. Updating that corpus does not itself require changing model weights. This makes RAG a natural approach when the answer depends on private or changing material, or when the application needs to show source documents. See OpenAI’s guide and AWS’s guidance on custom-document options.
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1Fix the driver behind crashes, sound loss and screen glitches2Clear out junk files and repair common Windows errors3Scan for outdated or missing drivers - takes under a minuteRAG does not guarantee that the model will find or use the right information. Retrieval may return irrelevant passages, omit necessary context, or return too much noise. Even relevant evidence can be misread or ignored by the model. Treat retrieval and answer generation as separate parts to evaluate: first ask whether the retrieved context is relevant and sufficient, then whether the answer uses it correctly.
#1 Best Overall
For question answering over custom documents, AWS recommends starting with RAG. Its guidance also cautions that RAG does not work well for summarizing entire documents and suggests considering fine-tuning for additional tasks such as summarization. That is a recommendation for this type of use case, not a rule that every document workflow should follow.
How fine-tuning works—and where it falls short
Fine-tuning continues training on a smaller task- or domain-specific dataset. In supervised fine-tuning, labeled examples show the model the desired input-output behavior. AWS also describes unsupervised fine-tuning as a way to adapt a model to a domain, with potential effects on generated content and organizational style. See OpenAI’s guide and AWS’s options guidance.
Fine-tuning is useful to evaluate when a model repeatedly needs to perform a task, follow a format, or produce a style more consistently. It is not a live searchable source of truth: changing information requires managing training updates and evaluation data, and a fine-tuned answer does not inherently include a reference to the source it learned from. Good results also depend on training examples that represent real production inputs.
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When a prompt alone is enough
In-context learning means providing material or examples in the request without changing model weights. For an occasional question about a single document, including that document in the prompt may be simpler than building a retrieval system or preparing a fine-tuning dataset. AWS describes this as appropriate for ad hoc single-document querying, while noting that it is not a good fit for querying many documents or enterprise systems. The useful boundary is practical: if the needed material fits the request and the task is occasional, test prompting first; if the application must reliably search a larger or changing collection, evaluate RAG.
A practical evaluation sequence
- Define the failure and its cost. Specify what a bad answer means for the application and how serious it is. Choose a task-relevant quality measure rather than assuming a technique is worthwhile because it is available.
- Build a prompt-only baseline. Test representative production cases and record the results before adding retrieval or training. Keep evaluation cases separate from examples used to fine-tune.
- Separate information failures from behavior failures. Did the system fail to find the needed information, or did the model find it but use it badly? The first points toward retrieval; the second may point toward prompting or fine-tuning.
- If testing RAG, inspect both stages. Check whether retrieved passages are relevant and sufficient, whether retrieval adds distracting material, and whether the answer is supported by the context. Tune retrieval and model behavior as distinct components.
- If testing fine-tuning, use representative examples. Make training inputs resemble real use, then measure performance on held-out cases to detect regressions or overfitting. Manage the training and validation sets as the task evolves.
- If combining them, train for the combined setup. When fine-tuning a model that will receive retrieved context, include examples with that context so the model learns how to use it. Compare the combined system against both single-method approaches.
- Track operational costs alongside quality. Measure the application’s target quality metric as well as latency and token use. Recheck results when prompts, retrieval, training data, or the model changes.
OpenAI notes that RAG requires retrieval tuning as well as model behavior tuning, while fine-tuning requires management of training and validation sets and may need reruns. The reviewed guidance does not establish a universal accuracy, latency, or total-cost threshold for choosing one method.
What the published score example does—and does not—show
OpenAI’s optimization guide reports BLEU scores for one Icelandic correction example: GPT-4 zero-shot scored 62; GPT-4 with three few-shot examples scored 70; GPT-3.5 Turbo fine-tuned with 1,000 examples scored 78; GPT-4 fine-tuned with 1,000 examples scored 87; and the fine-tuned GPT-4 setup with RAG examples scored 83. The guide’s accessed page does not state a publication year for this example. These figures describe that particular task and test setup, not a general RAG-versus-fine-tuning benchmark. In that example, adding RAG to the fine-tuned setup lowered the score relative to fine-tuning alone; it does not establish that this will happen for other tasks.
AWS published a different, vendor-specific case study on April 10, 2025, testing Amazon Nova Micro and Nova Lite configurations that included a baseline, RAG with Bedrock Knowledge Bases, and customization trained on 1,000 AWS-specific question-answer pairs. The authors report improvements from both methods and recommend combining them for question answering in the context of that experiment. See the AWS case study. Its recommendation is specific to the tested models, data, and setup; it is not an independent cross-industry benchmark or a universal prescription.
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Start with the simplest approach that can meet the requirement. If the model lacks access to the right facts, test retrieval. If it has the facts but applies the task inconsistently, test behavior changes, including fine-tuning where the baseline supports it. If both are necessary, test the combination rather than assuming that more components improve the result. Provider guidance cannot settle every model-specific constraint, privacy or security design, or total-cost question; validate the intended system on representative cases before putting it into production.
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