chapter nine
9 Advanced Prompt Patterns
This chapter covers
- Chain of Thought: making multi-step reasoning explicit when no reasoning model is available or the pattern is domain-specific
- Meta Prompting: generating prompt templates from task descriptions using the elements and patterns from earlier chapters
- The ReACT pattern: structuring Thought-Action-Observation loops for agentic workflows
- Knowing when each pattern earns its added complexity and when simpler prompting wins
Chapters 2 through 8 built the core toolkit: prompt structure, language shaping, reusable templates, few-shot examples, and multi-generation aggregation. Each technique improves what a model produces in response to a well formed request.
This chapter introduces three patterns that operate at a higher level of abstraction. Each one changes not just the content of a prompt but how reasoning or generation itself is structured.
- Chain of Thought makes intermediate reasoning steps explicit, producing auditable logic that engineers can review, challenge, and trace.
- Meta Prompting uses a prompt to design a prompt, applying the framework built across earlier chapters to accelerate template creation for recurring tasks.
- ReACT structures agentic workflows as interleaved Thought-Action-Observation loops, giving a model a principled way to incorporate real world observations before committing to a conclusion.