AI Glossary

A prompting technique that improves LLM accuracy on complex tasks by guiding the model to show its reasoning step-by-step before arriving at a final answer.

A prompting technique where you include a few examples of the desired input-output format in your prompt, helping the model understand exactly what you want without any fine-tuning.

The set of practices and tools for deploying, monitoring, and maintaining machine learning models in production — essentially DevOps principles applied to the ML lifecycle.

An open standard for connecting AI assistants to external data sources and tools through a unified, composable interface.

The practice of designing and refining inputs to language models to elicit more accurate, useful, and consistent outputs.

A technique for constraining a language model's output to follow a specific format like JSON, XML, or a defined schema, ensuring the response can be reliably parsed by downstream code.

The foundational instruction set given to an LLM that defines its role, behaviour, tone, and constraints for a particular application — set once at the application level and shaping all subsequent user interactions.

A parameter that controls how random or deterministic an LLM's output is — lower values produce more predictable, focused responses while higher values increase creativity and variation.