Prompt Engineering Technique Selection Guide

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Technique Reference

Technique Definition When to Use
Zero-shot Clear, direct instructions with no examples Simple, well-defined tasks where the model has strong baseline knowledge
Few-shot Examples included in the prompt to guide format, tone, or style Tasks requiring specific formatting or evaluation criteria not obvious from instructions alone
Chain-of-Thought (CoT) Model articulates its reasoning step-by-step along a single path Math problems, logical reasoning, multi-step analysis, debugging
Tree-of-Thought (ToT) Model explores multiple reasoning paths in parallel, evaluates each, and can backtrack from dead ends Planning problems, creative tasks with competing constraints, comparing alternatives
Persona-based Assigns a role to shape vocabulary, depth, and assumed audience knowledge Domain expertise, audience-specific explanations
Task decomposition Breaks a complex task into smaller, ordered subtasks Any task difficult to accomplish well in a single prompt
Self-consistency Samples multiple outputs or reasoning paths and selects the most common answer High-stakes decisions, complex reasoning where a single response might be wrong
Prompt scaffolding Wraps user input in a structured template with explicit constraints Defending against adversarial or off-topic input

Task Type to Technique

Task Type Recommended Technique
Simple, well-defined Zero-shot with clear instructions
Specific format or style Few-shot with examples
Complex reasoning Chain-of-thought
Multiple valid approaches Tree-of-thought
Domain expertise Persona-based + RAG
Multi-step workflow Task decomposition
High-stakes decisions Self-consistency

Common Pitfalls

Pitfall Solution
Vague instructions Be explicit about what you want
Too many constraints Prioritize the most important requirements
No examples for complex formats Add 2-3 diverse examples
Assuming model knowledge Provide necessary context
Single-shot for complex tasks Break into steps or use chain-of-thought

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