Prompt Engineering Deep Dive.

You will learn the complete prompting toolkit, from structure and roles to hallucination control and complex industry prompts, plus how to measure prompt quality with evaluations. End state is one production-grade prompt for your own domain, scored against a 5-case evaluation you wrote. After this module you can build prompts systematically instead of by feel, prove one version beats another with data, and recognize when the right tool is a skill or an agent rather than a prompt.

1. Master the full prompting toolkit: structure, roles, separating data from instructions, output formatting, step-by-step thinking, examples
2. Reduce hallucinations with explicit techniques (permission to say "I don't know", evidence-first answers)
3. Build complex prompts for real industry cases (legal, financial, support)
4. Write a basic prompt evaluation so quality is measured, not felt
5. Know the advanced patterns by name: prompt chaining, tool use, retrieval

• Anthropic, "Prompt engineering overview" — the model-independent techniques, and the index for the rest of this list: https://platform.claude.com/docs/en/build-with-claude/prompt-engineering/overview
• Anthropic, "Prompting Claude Opus 5" — over-verification, subagent delegation, response length, and what to strip from prompts written for older models: https://platform.claude.com/docs/en/build-with-claude/prompt-engineering/prompting-claude-opus-5
• Anthropic, "Effort" — the primary control for token cost and latency: https://platform.claude.com/docs/en/build-with-claude/effort
• Anthropic's interactive prompt engineering tutorial: https://github.com/anthropics/prompt-eng-interactive-tutorial
• Anthropic courses repo (real-world prompting, prompt evaluations): https://github.com/anthropics/courses