Prompt Engineering Roadmap

A staged learning path for Prompt Engineering, from basic prompting to advanced patterns and production operations.

Stage 1: Fundamentals (1-2 weeks)

Understand how LLMs work and master zero-shot and few-shot prompting.

Goals:
- Understand tokens, context windows, and temperature.
- Learn zero-shot best practices.
- Use few-shot examples to stabilize output.
- Compare basic behavior across models.

Practice:
- Build a zero-shot text classifier.
- Create a few-shot sentiment analyzer.
- Compare three models on the same task.

Stage 2: Core techniques (2-4 weeks)

Learn reasoning enhancement and behavior control techniques for harder tasks.

Goals:
- Use chain-of-thought and tree-of-thought.
- Design system prompts.
- Control output format and role behavior.
- Tune temperature for task fit.

Practice:
- Build a math reasoning assistant with CoT.
- Design a customer support system prompt.
- Create a structured data extraction pipeline.

Stage 3: Advanced patterns (3-4 weeks)

Move from prompt snippets to workflow architecture.

Goals:
- Use prompt chaining and multi-agent collaboration.
- Build RAG workflows.
- Understand tool use and context window management.
- Learn self-consistency and evaluation.

Practice:
- Build a RAG knowledge-base assistant.
- Create a multi-step content generation chain.
- Implement an agent with tool calls.

Stage 4: Production practice (ongoing)

Apply Prompt Engineering in production with testing, versioning, monitoring, cost control, and safety.

Goals:
- Build prompt versioning and test processes.
- Monitor quality, cost, and latency.
- Understand safety and compliance boundaries.
- Prepare rollback and incident handling.

Practice:
- Build a prompt evaluation pipeline.
- Run A/B tests for prompt variants.
- Launch and monitor a production AI feature.