AI Prompting Glossary

A prompting technique where the AI model is asked to perform a task without being provided any prior examples. The model relies entirely on its pre-trained knowledge to generate the response.

A technique where the AI is provided with a small number of examples (usually 2 to 5) within the prompt to demonstrate the desired format, style, or logic before asking it to perform the actual task.

A prompting strategy that forces the AI to break down complex, multi-step problems into intermediate reasoning steps before arriving at a final answer.

A phenomenon where an AI model generates false, nonsensical, or unverified information, presenting it confidently as fact. Hallucinations are often mitigated by providing strong context (RAG) or using structured frameworks like STCO.

An architecture that connects an LLM to an external knowledge base or database. Instead of relying on its training data, the model retrieves the exact factual documents needed to answer a query.

A parameter in AI inference that controls the randomness of the output. A temperature of 0 produces deterministic, highly predictable responses (good for coding/legal), while a temperature of 1 produces highly creative and varied responses (good for brainstorming/poetry).

System, Task, Context, Output. A proprietary prompt engineering framework ensuring perfectly aligned AI generation by defining exactly who the AI is (System), what it must do (Task), what data constraints it has (Context), and the exact format it should return (Output).