AI Glossary — Key Terms Explained for Business Leaders

AI systems that autonomously plan and execute multi-step goals

Autonomous AI systems that plan, act, and use tools to complete tasks

When LLMs generate confident but factually incorrect information

Running AI inference models on-device without cloud dependency for real-time, offline operation

Techniques that make AI model decisions understandable to humans — essential for regulated industries

Large pre-trained AI models like GPT-4, Claude and Llama that power modern AI applications

Structured data networks that store entities and relationships, enabling AI systems to reason across connections

Adapting pre-trained language models to specific domains or tasks

Practices that automate ML model deployment, monitoring and lifecycle management in production

AI systems that process and generate text, images, audio and video together

Techniques for writing effective prompts to guide LLM outputs

Retrieval-Augmented Generation: grounding LLM responses with external data

Reinforcement Learning from Human Feedback: aligning LLM behaviour with human preferences

Databases optimised for storing and searching AI embeddings