AI Glossarytechniques
Prompt Engineering
The practice of crafting effective instructions for AI models to produce desired outputs consistently.
How It 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.
Common Use Cases
- 1AI application development
- 2Content generation
- 3Data extraction
- 4Classification tasks
Related Terms
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.
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