The Prompt Engineering Playbook: 12 Patterns That Actually Work

The Prompt Engineering Playbook: 12 Patterns That Actually Work

Introduction

Prompt engineering is the art and science of crafting inputs that reliably produce desired outputs from large language models. This playbook covers 12 patterns that have proven effective in production environments.

Pattern 1: Zero-Shot Classification

Provide a clear instruction with no examples. Works well for straightforward classification tasks.

Classify the following text as positive, negative, or neutral: "The product arrived damaged and customer service was unhelpful."

Pattern 2: Few-Shot Learning

Provide a few examples to guide the model's output format and reasoning.

Pattern 3: Chain-of-Thought

Encourage step-by-step reasoning before providing a final answer.

Pattern 4: Self-Consistency

Generate multiple reasoning paths and select the most consistent answer.

Pattern 5: Tree of Thoughts

Explore multiple reasoning branches in parallel.

Pattern 6: ReAct (Reason + Act)

Combine reasoning with tool use in an interleaved fashion.

Pattern 7: RePrompting

Iteratively refine prompts based on model output quality.

Pattern 8: Role Prompting

Assign the model a specific expert role to improve domain-specific outputs.

Pattern 9: Structured Output

Request JSON, XML, or other structured formats for programmatic consumption.

Pattern 10: Constraint Satisfaction

Define hard constraints that must be satisfied in the output.

Pattern 11: Negative Prompting

Explicitly state what NOT to do to avoid common failure modes.

Pattern 12: Meta-Prompting

Ask the model to generate its own improved prompts for a given task.

Compiled by GhettoBox AI Intelligence Team