The four-element framework, chain-of-thought, few-shot prompting, system prompt design, RAG context injection, and prompt evaluation. Built from patterns that work on real production systems — not toy benchmarks.
This is a text-first course that links out to the best supporting material on the internet instead of trying to replace it. The goal is to make this the best course on prompt engineering you can find — even without producing a single minute of custom video.
This course is built by engineers who ship prompt engineering systems for a living. It reflects how these tools actually behave in production — not how the documentation describes them.
Every day includes working code examples you can copy, run, and modify right now. The goal is understanding through doing, not passive reading.
Instead of re-explaining existing documentation, this course links to the definitive open-source implementations and the best reference material on prompt engineering available.
Each day is designed to finish in about an hour of focused reading plus hands-on work. Do the whole course over a week of lunch breaks. No calendar commitment, no live classes.
Each day stands alone. Read them in order for the full picture, or jump straight to the day that answers the question you have today.
Tokenization, context windows, attention, and why prompt structure matters mechanistically. The four-element framework (persona, task, context, format) and why each element changes model behavior.
Standard CoT, zero-shot CoT, self-consistency sampling, tree-of-thought, and when to use each. The reasoning techniques that improve accuracy on hard tasks by 20-40% on benchmarks.
Example selection strategy, dynamic few-shot retrieval, output format control (JSON, markdown, structured lists), and the formats that reduce parsing errors in production pipelines.
System prompt architecture, persona and constraint injection, retrieval-augmented generation (RAG) context formatting, citation instructions, and handling conflicting information between system and retrieved context.
LLM-as-judge evaluation, building a prompt regression test suite, prompt versioning, A/B testing prompts in production, cost optimization, and the failure modes that only appear at scale.
Instead of shooting our own videos, we link to the best deep-dives already on YouTube. Watch them alongside the course. All external, all free, all from builders who ship this stuff.
Comprehensive walkthroughs of the core prompting techniques — CoT, few-shot, and system prompt design — tested on GPT-4, Claude, and Gemini.
The Google Brain paper that introduced CoT, with demonstrations of why making models reason step-by-step improves accuracy on hard reasoning tasks.
How to structure retrieved context in prompts, handle conflicting information, and build RAG pipelines that actually improve LLM responses.
Anthropic's guidance on system prompt architecture, XML tags, and the prompting patterns that work best with Claude models.
Building prompt evaluation pipelines, LLM-as-judge patterns, and regression testing approaches that catch prompt regressions before production.
The best way to deepen understanding is to read the canonical open-source implementations. Clone them, trace the code, understand how the concepts in this course get applied in production.
The largest community collection of tested prompts across domains. Good for understanding the range of what prompting can accomplish.
Brex's internal prompt engineering guide, open-sourced. Covers system prompt design, injection defense, and production patterns from a company shipping LLM features.
Open-source LLM testing and evaluation framework. Run regression tests across prompts, models, and configurations in CI/CD.
The most-used LLM application framework. Reading its prompt template implementations shows how production systems structure and version prompts.
You're integrating GPT-4, Claude, or Gemini into a product. This course teaches the prompting patterns that make those integrations reliable and cost-efficient.
Prompt engineering is the interface layer of every AI system. This course covers the production patterns for agent system prompts, RAG context, and evaluation.
Understanding prompt engineering helps you scope AI features realistically, review implementation quality, and set expectations with stakeholders.
The 2-day in-person Precision AI Academy bootcamp covers AI and prompt engineering in depth — hands-on, with practitioners who build AI systems for a living. 5 U.S. cities. $1,490. 40 seats max. June–October 2026 (Thu–Fri).
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