Course Outline

Prompt Engineering Crash Course

Learn how to design, test, and operate reliable prompts for large language model (LLM) applications in two weeks. This practical course moves from clear single-turn instructions to structured outputs, multi-step workflows, evaluation, security, and prompt management.

Duration
2 Weeks
Level
Beginner to Advanced
Format
Practical, production-minded

What You Will Learn

Rather than treating prompts as clever wording, the course treats them as versioned application behavior: define a task, supply the right context, constrain the output, measure results, and iterate against observed failures.

Crash Course Outcomes

  • Explain what prompt engineering can improve and when prompting alone is not the right solution
  • Write clear prompts that define a role, task, context, constraints, and output contract
  • Select among zero-shot, few-shot, reasoning, decomposition, and tool-use patterns
  • Produce machine-consumable structured outputs and handle invalid or incomplete responses
  • Design a lightweight prompt evaluation set with task-relevant quality, latency, and cost measures
  • Defend a prompt-based application against prompt injection and unsafe instructions
  • Version, test, observe, and improve a production-minded prompt workflow

Recommended Audience

  • Beginner: Product builders, analysts, and engineers who use LLMs but want repeatable results
  • Intermediate: Application developers building summarization, extraction, classification, or assistant features
  • Advanced: Applied AI practitioners responsible for prompt quality, reliability, safety, and cost in production

Prerequisites and Format

  • Basic familiarity with LLM chat interfaces; programming is helpful but not required for the core lessons.
  • Plan for roughly 4–6 hours each week: short readings, prompt experiments, and one practical deliverable.
  • Use a low-risk, non-sensitive sample dataset. Never place credentials, private customer data, or unredacted production data in a prompt experiment.
  • Go through Choosing between rules, ML, and GenAI first if you are unsure whether a prompt-based solution is appropriate for your problem.

Week 1: Design Clear, Testable Prompts

Core Prompting

Goals:

  • Turn ambiguous requests into explicit task specifications
  • Control response structure and use examples effectively
  • Apply prompt patterns to common language tasks

Syllabus:

  • What prompt engineering is?
  • The anatomy of a robust prompt: instruction hierarchy, task, context, constraints, examples, and a clear output contract
  • Model settings and their trade-offs: temperature, sampling, token limits, determinism, latency, and cost
  • Zero-shot prompting for classification, extraction, rewriting, summarization, and transformation; identify when adding examples is unnecessary or harmful
  • Few-shot prompting: choosing representative examples, keeping labels consistent, avoiding example leakage, and versioning example sets as data
  • Structured outputs: schemas, allowed values, validation, repair loops, and when to fall back safely
  • Iterative prompt design: form a hypothesis, make one change, compare outputs, and record the result

Week 2: Reason, Evaluate, Secure, and Operationalize

Prompt Ops

Goals:

  • Select advanced prompting patterns based on the task rather than novelty
  • Evaluate prompt changes systematically
  • Ship a safer, observable prompt workflow

Syllabus:

  • Reasoning-oriented patterns: task decomposition, plan-and-solve, self-checking, and when hidden reasoning should not be requested or relied upon
  • Prompt chaining and routing: split complex work into narrow, observable stages; pass only necessary context between them; route by task, risk, latency, and cost rather than a fixed prompt alone
  • Tool-aware prompts: define tool boundaries, validate arguments, distinguish tool output from instructions, and require confirmation for consequential actions
  • Context selection: prioritize authoritative, current information; use retrieval for external knowledge rather than overloading a prompt
  • Prompt evaluation: construct representative test cases; define accuracy, format-validity, safety, latency, and cost metrics; inspect failures by slice
  • Prompt optimization and management: version templates, use regression tests, compare variants, and separate prompt configuration from application code
  • Security and safety: identify instruction conflicts, prompt injection, data exfiltration risks, unsafe outputs, refusal behavior, and human escalation paths

Continue Learning

This outline is designed to complement broader public curricula. For extra hands-on practice, work through DeepLearning.AI’s ChatGPT Prompt Engineering for Developers, which covers iterative prompting and common application patterns, and the DAIR.AI Prompt Engineering Guide, which provides technique references and model-specific guidance.

After completing the course, choose the next path according to the problem: deepen evaluation and observability for production applications, learn RAG for evidence-backed answers, or study AI agents when controlled tool use and multi-step task execution are genuinely required.