Yesterday a free prompt-engineering course landed on Reddit and it actually looks like work. No “Master ChatGPT in 30 days” branding. No vendor pitch. Just 14 structured blocks, ~30 hours of study material, and 92 quizzes built across the whole thing. The Reddit thread got traction fast, and for once the top comments were people sharing which block they started with, not arguing about which model is best. There are no affiliate links anywhere in the course either, which is worth noting because that is the obvious monetization play for something with this much effort behind it.
The twist? Block VIII: Cross-Model Patterns.
Every other course teaches you to prompt one model well. This one teaches you to port every technique across every major API: Claude, OpenAI, Gemini, DeepSeek, Qwen, Kimi, Llama. Cross-model Rosetta stones. Fallback chains. Cost and quality routing tables. Think about what that actually means in practice: you spend three weeks dialing in a Claude workflow, your client switches budget to GPT-4o, and instead of starting from scratch you have a translation layer for every technique you already know. The author even flags that Block VIII will “drift fastest as new frontier models ship.” That is an unusual level of intellectual honesty, and it signals this is written by someone who actually ships production AI work, not someone who studies AI from the outside.
How to Use This Without Spending 30 Hours Upfront
🎯 Step 1, Block II (Prompt Core): Covers all 12 sampling parameters (temperature, top_p, top_k, max_tokens, stop, frequency/presence penalty, and more) with practical use-case bands for each. Most people set temperature to 0.7 and wonder why outputs vary wildly. This block fixes that permanently. You will learn that top_k and top_p are not the same thing, that frequency penalty and presence penalty solve different creative problems, and that max_tokens has a nuanced relationship with generation quality that nobody talks about. One afternoon here saves you weeks of trial and error later.
🔁 Step 2, Block VIII (Cross-Model Patterns): The real payoff if you work across multiple APIs. Rosetta stones for every technique, fallback chains, and pitfall flags for tokenizer drift and refusal divergence. Tokenizer drift is the reason a carefully crafted few-shot prompt falls apart when you swap models without changing a single word: different models chunk text differently, which breaks your implicit token budget assumptions. Refusal divergence is why the same request gets a clean answer from one model and a refusal lecture from another. This block names both problems clearly and gives you the diagnostic questions to fix them fast.
🏗️ Step 3, Block IV (Production): Structured outputs, prompt caching, injection defense, RAG. For anything going live, this is the block you need. The injection defense section alone is worth the price of admission (free), covering both direct and indirect prompt injection patterns with concrete test cases. The structured outputs section goes beyond “just ask for JSON” and covers schema enforcement, retry logic when models hallucinate structure, and when to use tool-calling versus raw JSON prompting depending on the model. The RAG section covers retrieval quality, not just pipeline wiring, and addresses the failure modes that matter in production: chunk boundary problems, embedding model mismatch, and how to handle queries that span multiple retrieved chunks.
📊 Step 4, Use the Interactive Tools: Cost calculator for 18 models grouped by region, prompt diff comparator, and a prompt simulator. These are not decorations. They are the lab component of the course. The cost calculator matters more than it sounds: the same use case can be 10x cheaper on a regional model versus a frontier model, and the quality delta is sometimes close to zero for structured extraction tasks. The diff comparator lets you paste two prompt versions side by side and see what changed, which is the fastest way to understand why one version outperforms another without relying on gut feel.
Pro tip: The course is fully bilingual ES + EN with a live language toggle built in. MIT licensed, so you can adapt it for internal team training without asking anyone’s permission. That second point is more valuable than it looks. If you want to onboard a new hire, a contractor, or a whole marketing team to serious prompting practices, you can strip the course, cut the irrelevant blocks, add your own examples, and ship it as your internal training program. No licensing headaches, no “contact us for enterprise pricing” walls.
The course is at gs-run.github.io/prompt-engineering-course. The GitHub repo is open for issues and PRs.
If prompt engineering has meant scattered Twitter threads and provider-specific tutorials up to now, this is the structured thing worth an afternoon of your time. 🔖
Frequently Asked Questions
Q: How much do models really differ when you use the same prompt?
Way more than most people expect. The course covers this extensively in Block VIII with cross-model comparisons showing how Claude, OpenAI, Gemini, DeepSeek, and others handle the same prompt differently: different refusal patterns, different tokenization, different response styles. A prompt that works great on one API can completely fail on another.
Q: If prompt engineering is just clever wording, why learn it?
Because it’s not just clever wording. The course makes a point of this: evals, cost routing, structured outputs, caching, and guardrails are where most of the real work lives now. A well-engineered prompt makes all that production stuff simpler and cheaper. You need both, and the course respects that reality instead of selling you a shortcut.
Q: How does the course handle the fact that APIs change constantly?
Honestly. Block VIII (the cross-model comparison section) drifts fastest, but the course emphasizes principles over exact implementation, which lasts longer. Plus it’s on GitHub under MIT license, so the community can patch sections as APIs evolve. Think of it as a living document, not a frozen tutorial.
Open academic prompt-engineering course — 14 blocks, vendor-agnostic, ES + EN, MIT licensed
by u/Cheap-Score4694 in PromptEngineering