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Prompt Engineering

Prompt engineering — what works, what doesn't, and why.

Browse 24 tutorials → Other topics

Why learn Prompt Engineering?

  • Better prompts often beat bigger models.

  • A short, learnable craft with high leverage.

  • Critical for cost, latency, and accuracy in any LLM product.

  • Pairs with evals — measure, don't guess.

What you can build with Prompt Engineering

Tuning chatbots and copilots Improving classification and extraction Reducing hallucinations Designing agentic prompts and tools

Prompt Engineering tutorials

24 articles · page 1 of 2

Hand-written tutorials, ordered as a recommended learning path.

  1. 01 Prompt Engineering Basics A practical guide to prompting LLMs — system vs user prompts, clarity over cleverness, few-shot examples, structured JSON output, chain-of-thought, and eval-driven iteration.
  2. 02 Templates & Patterns How to turn prompts into reusable templates: variables, system messages as contracts, few-shot structure, JSON output schemas, role prompting, and when patterns help versus hurt.
  3. 03 Chain of Thought Master chain-of-thought prompting to improve LLM accuracy on complex tasks through step-by-step reasoning, zero-shot CoT, and structured thinking patterns.
  4. 04 Evaluation Techniques Learn how to measure prompt quality with evaluation datasets, scoring rubrics, A/B testing, and automated grading to iterate on prompts with evidence.
  5. 05 Few-Shot Examples Master few-shot prompting to teach LLMs new tasks through carefully selected examples, formatting patterns, and example ordering strategies.
  6. 06 Code Generation Learn prompt patterns for writing, reviewing, debugging, and refactoring code with LLMs, including practical templates and real examples.
  7. 07 Multi-Turn Conversations Learn how to design multi-turn LLM conversations with effective context management, memory patterns, conversation state tracking, and production architectures.
  8. 08 System Prompts Learn how to craft system prompts that reliably control LLM behavior through persona setting, constraints, output rules, and guardrails.
  9. 09 Structured Output Learn how to reliably extract JSON, XML, tables, and other structured formats from LLMs using schema enforcement, prompt patterns, and validation.
  10. 10 Temperature & Parameters Understand how temperature, top-p, max tokens, frequency penalty, and other LLM parameters affect output quality, and learn how to tune them for different tasks.
  11. 11 Prompt Chaining Learn how to chain multiple LLM prompts together to solve complex tasks. Covers sequential chains, branching, validation loops, and production patterns in Python.
  12. 12 Prompts for Code Master prompt engineering techniques for generating, debugging, and refactoring code with LLMs. Includes practical patterns, templates, and Python examples for developers.
  13. 13 Prompt Injection Understand prompt injection attacks against LLM applications. Learn detection strategies, input sanitization, defense-in-depth patterns, and how to build resilient AI systems.
  14. 14 Structured Output Learn how to prompt LLMs to return structured JSON output reliably. Covers schema enforcement, Pydantic validation, and production patterns for consistent structured responses.
  15. 15 Anti-Patterns A field guide to the most common prompt engineering anti-patterns, why they degrade LLM output quality, and concrete refactors that fix each one.
  16. 16 Evaluation Loops How to build evaluation loops for prompts so you can iterate with evidence instead of vibes. Covers datasets, graders, regressions, and how to make eval cheap enough to run often.
  17. 17 Output Formatters How to coax LLMs into producing predictable, parseable output using output formatters, JSON schemas, examples, and validation loops that actually hold up in production code paths.
  18. 18 Few-shot Decide between zero-shot and few-shot prompting by weighing example quality, cost, and how strictly you need to control output format.
  19. 19 ReAct Learn the ReAct pattern, a prompting technique that combines reasoning and action to build effective tool-using LLM agents.
  20. 20 Self-Consistency Learn how self-consistency prompting samples multiple reasoning paths and aggregates answers to improve accuracy, with hands-on examples and trade-offs.