A practical course that takes you from copy-pasting prompts off Twitter to designing reliable, evaluable, production-grade AI systems.
Used by builders shipping with
The problem
Works on three test cases. Falls over the moment a real user types something unexpected.
"It feels better" is not a metric. You ship blind and discover regressions in production.
"You are a world-class expert..." Cargo-culting incantations instead of understanding the model.
What you'll be able to do
Structure system prompts, examples, and constraints so behavior is consistent across edge cases — not just the happy path.
Move from "looks good" to measured. Set up eval datasets, automated graders, and regression tests.
Implement function calling, multi-step planning, and ReAct loops for systems that take real actions.
Recognize attack patterns and apply mitigations: input separation, output validation, and least-privilege tool design.
Trade off latency, cost, and capability across Claude, GPT, Gemini, and open models — with real benchmarks.
Build retrieval-augmented systems that actually return relevant context — chunking, embeddings, reranking, and context construction.
Free value
A preview of what's in the course — concrete principles you can apply this afternoon.
Two clear examples teach the model more than three paragraphs of instructions. Examples define the boundary — the boundary is what matters.
Specify the exact shape: word limit, format, allowed values. "Be concise" is unmeasurable — "respond in under 40 words" works.
Models attend more to the start and end of context than the middle. Put the actual task and critical rules near the boundaries.
Hallucinations drop dramatically when you give the model a clear out. Tell it what to say when it doesn't know.
Wrap user-supplied content in tags. Then explicitly tell the model: text inside the tags is data, not instructions. Cheap injection defense.
Stop tweaking based on vibes. Build a small eval set, observe which prompts pass and fail, and change one thing at a time.
The course expands each of these into a full lesson — with code, examples, and the failure cases nobody warns you about.
Curriculum
Roughly 20–22 hours of material plus capstone projects. Self-paced. Lifetime access.
Real applications
The patterns in this course aren't theoretical. They're the same techniques behind production systems handling real work, right now.
Classify inbound tickets, extract structured fields, and draft replies that match your tone. Cut response time without losing the human touch.
Catch bugs, security issues, and unclear naming in pull requests. Suggest refactors that match your codebase's style — not a generic best practice.
Pull facts, summaries, or specific fields out of contracts, research papers, financial reports. Citations included so nothing is unverifiable.
Blog drafts that match your voice. Email templates that respect your style guide. Marketing copy that doesn't read like every other LLM output.
Pull structured records from invoices, emails, PDFs, and form submissions. JSON output that validates. Missing fields handled gracefully.
Structured analysis of complex situations: pros/cons, risk assessment, recommendation with reasoning. Auditable, not a black box.
Each use case in the course comes with the actual prompts, the failure modes that show up in production, and the evaluation strategy.
Who this is for
You're integrating an LLM into a real product. You need it to behave under load, edge cases, and adversarial input.
You're scoping AI features. You want to know what's possible, what's expensive, and what's a footgun.
You use LLMs daily for thinking, writing, and synthesis. You want to get more out of every interaction.
What students say
"I'd read every prompt-engineering blog post on the internet. This was the first time it felt like a coherent discipline instead of folklore."
"The eval module alone paid for the course three times over. We caught a regression in our support bot the day we shipped the new prompts."
"I went from 'I have an idea for an AI feature' to 'I shipped one' in two weekends. The production module is gold."
Pricing
No subscription. No tiers. No upsells. 7-day money-back guarantee, plus a free Module 1 preview.
One-time payment · No subscription
Secure checkout · 7-day money-back guarantee
FAQ
The first three modules require no code. Production, evaluation, and safety modules include Python examples, but the concepts translate to any language.
Claude (Anthropic), GPT (OpenAI), Gemini (Google), and open models like Llama and Mistral. Techniques are model-agnostic; differences are called out where they matter.
The fundamentals — how transformers behave, how to evaluate, how to defend — don't change with each model release. You also get free updates whenever I revise the material.
Most students finish in 2–3 weeks at a relaxed pace. It's self-paced, so you can binge it in a weekend or stretch it out.
Yes — Module 1 is free, no signup required. Read it before you decide. It's a real lesson, not a teaser, so you can confidently judge whether the style and depth match what you're after.
7-day money-back guarantee. If the course isn't a fit for you, email within 7 days of purchase and we'll refund you — no forms, no questions. We also offer the free Module 1 so you can preview the course before buying. See the full refund policy.
Yes — you'll get a proper invoice after enrollment. $197 is within most professional-development reimbursement limits.
The teams winning with AI aren't the ones with the cleverest one-liner. They're the ones with reliable systems. Build yours.
Enroll now — $197