Prompt Engineering for Large Language Models
Structured prompting, few-shot examples, tool use, and output control
Structured prompting, few-shot examples, tool use, and output control
Prompt engineering is the practice of crafting precise inputs that guide large language models to produce accurate, controlled, and useful outputs. This includes structured formats, example-based prompting, and methods to enforce reasoning or tool usage.
Use clear sections, instructions, and constraints to control model behavior.
Provide sample inputs/outputs so the model learns patterns.
Guide the model to call APIs, databases, or functions when needed.
Define formats, tone, constraints, and validation rules.
Define objective and constraints clearly.
Choose structured format and required details.
Add few-shot examples or demonstrations.
Specify tool use or reasoning requirements.
Validate and refine the model’s outputs.
Articles, scripts, blog posts with structured style control.
Chain-of-thought or tool-based reasoning workflows.
LLM-driven apps using functions, search tools, or APIs.
They reduce ambiguity and increase output quality.
Whenever you want consistent style or behavior.
Directing the model to call external functions or data sources.
Control outputs, improve accuracy, and build powerful LLM workflows.
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