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 craft of designing inputs to large language models to optimize clarity, output quality, and reliability. It includes structuring prompts, using examples, guiding tools, and controlling the style or format of responses.
Structured Prompts
Few‑Shot Learning
Tool Use
Output Control
Use sections such as role, task, constraints, examples, and output format.
Show examples of desired behavior to shape model outputs.
Guide the model to call functions, APIs, or external reasoning tools.
Specify tone, structure, format, verbosity, or constraints.
1. Define Task
Clarify goal.
2. Structure Prompt
Use roles and format.
3. Add Examples
Provide demonstrations.
4. Integrate Tools
Define tool calls.
5. Refine Output
Iterate and adjust.
Structured prompting ensures predictable formats.
Examples teach tone, personality, and dialogue rules.
Tool use enables API calls and workflow actions.
Do all prompts need examples?
No, but examples help when you need specific behavior.
When should I use tools?
Use them when tasks require external information or actions.
How do I control verbosity?
Include explicit instructions like concise, short, long, or detailed.
Apply structured techniques to get more accurate and reliable outputs.
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