Advanced Prompt Strategies

Introduction

Once you've mastered CoT, ToT, reflection and agents, advanced strategies combine them — meta-prompts, prompt chaining DSLs, evolutionary search, and prompt programming.

Beginner analogy: Like moving from cooking dishes to designing menus — composition becomes the skill.

Understanding the topic

Core concepts to understand:

• Meta-prompting — prompts that write prompts.
• Prompt chaining — output of one feeds the next.
• Prompt programs — DSPy, LMQL, LangChain Expression Language.
• Evolutionary search — auto-optimise prompts against an eval.
• Programmatic constraints — Outlines, GBNF grammars.

Syntax reference

Visual workflow / architecture:

Meta-prompt:
  "Generate the best system prompt for a customer-support bot."
   ▼
Generated prompt
   ▼
Eval against test set
   ▼
Iterate or accept

Real-world use

Anthropic, OpenAI, and DSPy users all use meta-prompting and evolutionary search to optimise high-traffic prompts.

Best practices

• Start manual; add automation when you have evals and traffic.
• Treat prompt programs as software — tests, CI, monitoring.
• Don't over-engineer until manual prompts plateau.

Hands-on exercise

Interview preparation — practice these questions:

• Q1. What is meta-prompting?
• Q2. DSPy in 1 sentence.
• Q3. When use evolutionary search?
• Q4. Risk of auto-optimised prompts?