How Are LLMs Similar to Traditional AI Systems in Predictions?

What Core Prediction Mechanism Links LLMs and Classical AI?

Discover the key similarity between LLMs and traditional AI: both predict outputs from inputs—essential foundation for understanding agentic systems, CrewAI orchestration, and multi-agent LLM integration in production.

Question

How are Large Language Models (LLMs) similar to traditional AI systems?

A. Both predict outputs based on the inputs they receive.
B. LLMs and traditional AI systems both rely on manual rules to generate predictions.
C. LLMs do not use inputs to make predictions like traditional AI systems do.
D. Traditional AI systems use prompts as input features, just like LLMs.

Answer

A. Both predict outputs based on the inputs they receive.

Explanation

Large Language Models (LLMs) share a fundamental similarity with traditional AI systems in that both operate as predictive systems, generating outputs deterministically or probabilistically derived from input features—whether structured data vectors in classical ML models like decision trees or neural nets, or tokenized text sequences in transformer-based LLMs. Traditional systems map inputs through trained weights or rules to classify, regress, or decide (e.g., spam detection from email features), while LLMs autoregressively predict next tokens conditioned on prompt context, but the core input-output prediction paradigm remains consistent across both paradigms.