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Plain-language AI glossary

Term 08TechniquesMeaning / context / connections

Techniques / Definition

Prompt Engineering

The practice of crafting effective instructions for AI models to produce desired outputs consistently.

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01

MeaningThe one-sentence definition.

02

ContextHow the idea works in practice.

03

UsesWhere the concept becomes useful.

01 / Plain-language context

How Prompt Engineering works.

Prompt engineering is the most cost-effective way to improve AI output quality. Key techniques: (1) System prompts that define the AI's role and constraints, (2) Few-shot examples showing desired input-output patterns, (3) Chain-of-thought prompting for reasoning tasks, (4) Structured output formats (JSON mode). Good prompts are specific, include examples, define edge cases, and constrain the output format. This is often the difference between a prototype and a production-quality AI feature.

02 / Practical uses

Where it helps.

  1. 01AI application development
  2. 02Content generation
  3. 03Data extraction
  4. 04Classification tasks
03 / Keep exploring
Large Language Model (LLM)A neural network trained on massive text datasets that can generate, understand, and reason about human language.Fine-TuningThe process of further training a pre-trained AI model on your specific data to improve performance on domain-specific tasks.Function Calling (Tool Use)An AI capability where the model can decide to invoke external functions or APIs based on the conversation context.