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No. If a task has fixed rules and a predictable correct result, ordinary software or a rules engine may be simpler and more dependable than adding a large language model (LLM). Use traditional predictive AI for repeatable forecasts, classifications, or detections; use generative AI when the task calls for creating or summarizing language or working flexibly across modalities. Some workflows benefit from combining these approaches.
Start with the task, not the AI label
Describe the work before choosing a technology: what goes in, what result must come out, how often the task runs, what an error would cost, and what constraints apply. A system that drafts a natural-language explanation has different needs from one that validates an invoice total or routes a request according to fixed rules.
Microsoft’s AI decision framework recommends deterministic code or non-generative AI for structured, predictable workflows. That does not make LLMs a poor choice in general; it means the method should fit the work.
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| Approach | Good fit | Examples |
|---|---|---|
| Deterministic software | Fixed rules, validation, routing, formatting, and transformations with a known correct answer | Checking required fields, applying a threshold, or moving a case through a defined workflow |
| Traditional predictive or classification AI | Repeatable predictions, classifications, detections, regression, forecasting, or clustering on structured data | Spam classification, defect detection, house-price prediction, sales and demand forecasting, and customer segmentation |
| Generative AI, including LLMs | Generating or summarizing language, or flexibly handling text and other modalities | Content generation, summarization, and advanced transcription |
| Hybrid system | A workflow that needs more than one kind of capability | A predictive model estimates churn, while a generative interface lets staff explore the predictions in natural language |
The traditional-AI and generative-AI examples come from Google Cloud’s overview of AI. They are useful starting points, not guarantees of accuracy or performance for a particular system.
When deterministic code is the better choice
If a workflow follows known rules and the valid answer can be calculated or checked directly, code, a state machine, or a rule engine can keep behavior predictable and easier to observe and recover. For example, a program can check whether a submission contains required fields, apply a published eligibility rule, or format a date in a specified way. An LLM may add cost and variable outputs without improving those steps.
Rules-based code is not automatically the right answer if the task involves ambiguity or a changing pattern that rules cannot capture. In that case, consider predictive AI or an LLM for the part that needs inference, and use ordinary software to enforce rules where they are explicit.
When traditional AI is a stronger fit than an LLM
Use predictive or classification AI when the goal is to assign a category, estimate a value, detect a pattern, or forecast an outcome from structured examples. For instance, a model may estimate demand from historical sales data; an LLM is not necessary simply because both systems are called AI.
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When an LLM makes sense
An LLM is worth considering when the required result is language, such as drafting or summarizing, or when users need flexible interaction with text or other modalities. The benefit is not just that the task is complicated: it is that the task needs the kind of generative or flexible language capability an LLM provides.
For example, a fixed form can be validated with deterministic rules, while an LLM could help summarize a free-form explanation attached to it. Separate those jobs rather than asking one model to handle both when only one part needs language understanding.
How to choose and evaluate an approach
- Define the outcome and risk. Record the input, required output, expected frequency, acceptable error rate, and consequences of a wrong result. Identify any privacy, security, or data-governance requirements.
- Compare the simplest plausible options. Include ordinary code, traditional predictive AI, generative AI, and a hybrid design where relevant. Consider quality, latency, total cost, available data, customization, interpretability or control, deployment and region availability, and the effort needed to monitor and debug the system.
- Test on representative cases. Use examples that reflect real inputs, including difficult and unusual cases. Compare outputs against criteria that matter to the task, using benchmarks, evaluators, or playground testing where appropriate, as Microsoft’s model-selection guidance describes.
- Check operational constraints before deployment. Verify that the chosen approach can meet performance and latency needs and that its data handling, region, deployment method, and security controls are acceptable. A model that performs well in a sample test may still be unsuitable under those constraints.
- Keep people accountable for consequential judgment. For outputs requiring professional judgment or high accuracy, review should match the risk. The UK government’s AI Knowledge Hub recommends testing with known examples and checking sources and numbers. Verify facts and dates rather than treating fluent output as proof.
Use LLMs carefully in evaluation, too
LLMs can assist with some evaluation activities, but their suitability depends on what is being measured. In information-retrieval relevance evaluation specifically, a NIST-hosted paper on LLM-generated relevance judgments warns against treating those judgments as ideal ground truth: they can constrain what an evaluation measures and disadvantage systems that retrieve relevant documents the LLM did not judge relevant. That finding concerns relevance judgments; it is not evidence that LLMs are unsuitable for every kind of evaluation.
Quick Recap
A practical rule of thumb
- If the task has fixed rules and a known answer, try deterministic software first.
- If it predicts or classifies structured data, evaluate traditional predictive AI.
- If it needs generation, summarization, or flexible language interaction, evaluate an LLM.
- If a workflow contains different kinds of work, assign each part to the approach that fits it and test the whole system.
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