No. Start with the task and the result you need, then choose the simplest approach that can meet your quality, speed, security, cost, and operational requirements. Fixed rules may call for ordinary code; structured predictions may fit traditional machine learning; language generation or summarization may justify an LLM. Some workflows benefit from combining them.
Describe the task before choosing a model
Write down what goes in, what must come out, how often the task occurs, what counts as an error, and what constraints apply. A fixed answer with clear rules is different from a request to interpret varied language or draft a useful response. The right choice depends on that distinction—not on whether a tool is labeled “AI.”
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Microsoft’s AI decision framework advises considering task fit, cost, context, security, region availability, deployment, domain specificity, performance, and tunability when selecting an approach: Microsoft’s model-selection guidance.
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When ordinary code is the better fit
If the workflow follows stable rules and has a predictable correct result, use deterministic software where practical. Validation, routing, formatting, and known transformations often fit ordinary code, a rule engine, or a state machine. These approaches make the logic explicit and can be easier to observe and recover when something goes wrong.
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Microsoft puts the principle plainly: “Structured and predictable tasks: Use deterministic code or non-generative AI when the workflow is rule-based and predictable.” See its AI decision framework.
For example, a system that accepts a date only when it matches a defined format does not need an LLM to interpret the rule. A language model may add variability without improving the outcome.
When traditional predictive AI fits
Traditional AI is often suited to repeatable prediction, classification, detection, regression, forecasting, or clustering using structured data. Google Cloud lists examples such as spam classification, product-defect detection, house-price prediction, sales and demand forecasting, and customer segmentation in its overview of generative and traditional AI use cases.
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For a classification or detection problem, first check whether a pretrained model meets the task’s requirements. If it does not, consider whether you have enough suitable training data to customize a model. The choice also depends on whether you need more control over training or prioritize a faster path to deployment; neither option guarantees that the model will perform adequately on your data.
When an LLM is worth considering
Generative AI is a stronger candidate when the result itself must be generated, when information needs to be summarized, or when a system must work flexibly with language or other modalities. Google Cloud identifies content generation, summarization, and advanced transcription among generative AI use cases in its AI overview.
That flexibility comes with evaluation work: generated output may vary, and a fluent answer is not proof that its facts are correct. If correctness matters, define what must be checked and who is responsible for approval rather than treating the model’s response as an authoritative result.
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When a hybrid approach makes sense
You do not have to choose one method for an entire product. Google Cloud describes combining a predictive model that estimates churn with a generative interface that lets staff explore those predictions in natural language. That arrangement keeps the structured estimate separate from the conversational way a person asks about it.
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A similar design can use an LLM to interpret ambiguous language, then let deterministic code validate the request and execute known rules. This is an architectural option, not a guarantee of better performance: keep the boundary between interpretation and rule execution clear, and test the complete workflow.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Compare approaches against the real workload
There is no universal winner. Compare only the approaches that could plausibly meet the task’s requirements, using representative inputs and criteria that matter to the people relying on the result.
| What to assess | Question to ask |
|---|---|
| Task fit and output quality | Does the approach produce the required result on representative cases, including difficult or unusual inputs? |
| Latency and availability | Is the response fast enough, and is the approach available where and how it must be deployed? |
| Total cost | What does the full workflow cost to build, run, monitor, and maintain? |
| Data and customization | Is relevant data available, and does the task require a custom model or domain-specific behavior? |
| Control and interpretability | Can the team understand, constrain, and debug the decisions that affect the outcome? |
| Security and governance | Can the approach satisfy the task’s data-handling and security requirements? |
| Error consequences | What happens if the result is wrong, and how much human review is appropriate? |
Microsoft recommends comparing models with benchmarks, evaluators, and playground testing in its selection guidance. Treat these as ways to assess fit for your workload, not as substitutes for testing the actual workflow and its failure cases.
Test the result and keep people accountable
Use known examples, include cases where errors would matter, and check facts, dates, numbers, and sources in generated responses. The UK government’s AI Knowledge Hub cautions that “AI will not help with every task” and recommends testing with known examples and scaling review to the context and risk: AI Knowledge Hub.
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1Clear out junk files and repair common Windows errors2Fix the driver behind crashes, sound loss and screen glitches3Repair Windows errors before they cause bigger problemsFor decisions requiring professional judgment or high accuracy, assign a qualified person responsibility for reviewing the output. In information-retrieval evaluation specifically, a NIST-hosted paper warns against treating LLM-generated relevance judgments as ideal ground truth: they can limit what an evaluation measures and disadvantage systems that retrieve relevant documents the LLM did not judge. That caution concerns relevance judgments; it does not establish that LLMs are unsuitable for every evaluation task. Read the NIST-hosted paper.
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