Prompt Engineering for Large Language Models
Structured prompting, few-shot examples, tool use, and output control.
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Structured prompting, few-shot examples, tool use, and output control.
Start Learning
Prompt engineering is the practice of designing prompts that guide large language models toward accurate, controllable, and useful outputs. Techniques include structured instruction, examples, tool integration, and constraint-based output shaping.
Use clear sections like task, input, and constraints.
Demonstrate the desired format or reasoning with examples.
Guide the model to call APIs, run functions, or delegate tasks.
Specify formatting rules and constraints to maintain consistency.
Clarify goals.
Use sections.
Show patterns.
Implement functions.
Iterate outputs.
Articles, scripts, stories, technical guides.
Step-by-step reasoning and problem solving.
Tool execution, workflows, agent behaviors.
It helps the model understand expectations and reduces ambiguity.
Usually 1–3 examples are enough unless the format is complex.
When tasks require external data, calculations, or specialized logic.
Build better prompts, get better results.
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