This article provides a comprehensive guide to prompt engineering, detailing systematic techniques for effective communication with large language models. It breaks down prompt construction into basic, structured, and advanced patterns, offering practical examples for each. Key methods covered include zero-shot, few-shot, Chain-of-Thought, Persona + Context, Step-by-Step Decomposition, Output Format Specification, ReAct, and Self-Consistency, along with advice on integrating these into applications using tools like Python and FastAPI. AI
IMPACT Provides practical techniques for developers to improve LLM output quality and control.
RANK_REASON Article details techniques for using LLMs, not a new release or research.
- FastAPI
- Few-shot learning
- JSON
- Output Format Specification
- OWASP Top 10
- Persona + Context Pattern
- prompt engineering
- pydantic
- Python
- SQL
- Step-by-Step Decomposition
- zero-shot learning
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