Rules-Based AI
Definition
Rules-Based AI uses explicit logical rules, written by humans or learned from structured patterns, to make decisions. It is the historical foundation of deterministic AI systems.
Why it matters
The business case for Rules-Based AI.
Modern deterministic AI extends beyond pure rules-based systems, but the underlying principle remains: when an output must be predictable and defensible, structured logic outperforms statistical guesswork.
Related terms in Deterministic AI
Deterministic AI
Deterministic AI produces the same output for the same input, every time. Unlike probabilistic AI, deterministic systems deliver consistent, predictable, auditable outcomes that enterprise operations can rely on.
Probabilistic AI
Probabilistic AI generates outputs based on statistical likelihoods. The same input may produce different outputs across runs. Most large language models are probabilistic by design.
Generative AI
Generative AI is a class of probabilistic AI that creates new content, text, images, code, audio, based on patterns learned from training data. ChatGPT, Claude, and Midjourney are generative AI systems.
AI Hallucination
AI Hallucination occurs when a generative AI system produces output that is plausible-sounding but factually incorrect or fabricated. Hallucinations are a structural feature of probabilistic AI, not a bug.
Explainable AI (XAI)
Explainable AI refers to AI systems whose decisions and outputs can be understood and traced by humans. XAI is critical for governance, compliance, and trust.