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1Scan for outdated or missing drivers - takes under a minute2Clear out junk files and repair common Windows errors3Fix the driver behind crashes, sound loss and screen glitchesChoose based on what is failing: use retrieval-augmented generation (RAG) to bring in relevant, potentially changing source material at request time; fine-tune when examples are needed to make a repeated behavior more consistent; and test long-context prompting when the relevant material is bounded and fits the selected model’s input. None is a universal winner. Evaluate the options on representative tasks, then keep only the layers that improve the result enough to justify their cost and complexity.
What is the difference between RAG, fine-tuning, and long context?
| Approach | What it changes | Best-fit problem to evaluate | Main constraint |
|---|---|---|---|
| RAG | At request time, retrieves selected material from an external source and supplies it with the request. | Answers need current, private, or traceable information. | The system must retrieve the right passages, enforce access rules, and use the passages correctly. |
| Fine-tuning | Adapts model behavior using training examples. | A repeated task, format, or style needs more consistent behavior. | It requires an appropriate dataset and a training and maintenance workflow; it is not a live, automatically refreshed knowledge base. |
| Long-context prompting | Places a larger body of material directly in the model’s input. | The relevant corpus is bounded and can fit in the chosen model’s context. | Context capacity varies by model and can change; including material does not guarantee the model will use every detail accurately. |
These are different optimization levers, not necessarily stages in a required sequence. OpenAI’s Optimizing LLM Accuracy guide cautions against treating optimization as a simple linear path from prompting to RAG to fine-tuning. AWS also describes in-context learning, RAG, and fine-tuning as options for custom-document question answering in its guidance on generative AI options.
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How do I decide which approach to evaluate first?
| Workload signal | First approach to evaluate | What to measure |
|---|---|---|
| Facts change, are private, or need a traceable source | RAG | Whether retrieval finds the right passages, permissions are respected, and answers stay grounded in retrieved evidence. |
| Output format, tone, or a repeated task behavior needs to be more consistent | Fine-tuning | Whether representative examples improve the target behavior over a prompt baseline without harming other evaluation cases. |
| All relevant material is bounded and fits the selected model’s context | Long-context prompting | Accuracy across the material, plus the context-related cost and latency. |
| Both current evidence and stable output behavior matter | Evaluate a combination | Measure each layer alone and together; retain added layers only when they improve the target outcome enough to justify the added complexity. |
This framework identifies what to test first, not what will be cheapest or most accurate. Those outcomes vary by model, provider, implementation, and task. OpenAI’s optimization guidance recommends choosing levers in response to observed failure modes rather than assuming one fixed recipe.
When does RAG make sense?
RAG is worth evaluating when an answer should draw on information outside the model’s request, especially if that information changes or must be tied to a source. The system retrieves passages from a data source and supplies selected content alongside the user’s request. This can make relevant evidence available without treating the model’s trained behavior as a refreshed store of facts.
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Retrieval alone does not make an answer correct. Check whether the search step finds the right material, whether the user is allowed to access it, and whether the generated answer accurately reflects it. If the answer includes citations, verify that each citation supports the claim it accompanies. AWS’s custom-document guidance discusses RAG among the available approaches; implementation results still depend on the workload and system design.
When does fine-tuning make sense?
Consider fine-tuning when the desired improvement is a stable behavior—such as following a recurring task pattern or producing a consistent format—and prompting alone has not met the target on representative examples. Compare the fine-tuned model with a prompt baseline, and check for regressions on cases beyond the training examples.
Fine-tuning involves preparing training data and creating a training job. It should not be treated as a live knowledge base: adding a fine-tuning workflow does not automatically keep changing facts current. The OpenAI Fine-tuning API reference documents jobs, training files, and methods; supported methods and models can change, so consult current documentation when planning an implementation.
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When is long-context prompting the better first test?
If the relevant documents form a bounded set, provide them directly in the request as a baseline before building retrieval or a training workflow. This can be useful for document analysis when the material fits the chosen model’s context. Test questions that require details from different parts of the input, not just the opening passages.
Context limits are model-specific and may change. Check the current model documentation for the model you intend to use instead of relying on a context-window number from an older page. Even within a model’s limit, more input is not a guarantee that every relevant detail will be noticed or used correctly.
Can you combine the approaches?
Yes, if each layer addresses a demonstrated need. For example, a system could retrieve current evidence, include the retrieved passages in the prompt, and use an adapted model for a stable output behavior. That architecture adds components and operational work; the combination should be measured against each approach on its own rather than assumed to perform better. OpenAI’s accuracy guidance supports evaluating optimization choices against the task’s observed needs.
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How should you compare them fairly?
- Define the target. Gather representative requests and specify what counts as a correct, sufficiently grounded, or properly formatted answer.
- Record a baseline. Evaluate the existing prompt and model before adding retrieval, training, or a larger context.
- Test the likely lever. For RAG, inspect retrieved passages and access handling; for fine-tuning, compare against the prompt baseline; for long context, test across the supplied material.
- Measure operational trade-offs. Compare task quality alongside freshness, traceability, corpus size, context use, indexing or training effort, latency, and cost for the actual workload.
- Test combinations separately. Add a second layer only when its individual and combined results justify the extra system complexity.
There is no source-supported universal ranking for cost or accuracy across all three approaches. The official guidance cited here does not establish a single benchmark that ranks them across workloads. Context pricing, retrieval performance, training requirements, latency, and answer quality depend on the model, provider, implementation, and evaluation task.
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