Guide
LLM vs traditional ML — how to choose
The honest answer to "should we use an LLM?" is almost always "it depends on your data shape and your tolerance for errors". This page makes that decision concrete.
Use classical ML when…
Your data is tabular and structured. You have a clean target column (churn, fraud, anomaly). Decisions are repeated, high-volume, and need to be explainable. The cost of a wrong answer is high. Examples: attrition prediction, intrusion detection, payroll anomaly flagging.
Use LLMs (with RAG) when…
Your data is unstructured: CVs, reports, contracts, transcripts, emails. Users ask open-ended questions. The output is text or summary, not a hard label. Multilingual (Arabic/English) needs are real. Examples: CV extraction, incident-report search, policy Q&A.
Combine both (hybrid)
Real production systems are almost always hybrid: an LLM extracts and normalises unstructured input into structured features, then a classical model makes the deterministic decision. This is the most common pattern we deploy.
FAQ
Are LLMs cheaper than classical ML?+
No, almost always more expensive per inference. Use LLMs where they create value classical ML cannot — unstructured input, natural-language output — not for cost savings.
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