Prompt Engineering Crash Course
The prompting techniques senior AI engineers use daily, and most tutorials skip.
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Prompt engineering is a real skill, and also narrower than Twitter suggests. Most of the value comes from a handful of patterns: structured output, few-shot examples, clear task decomposition, and systematic evaluation. The rest is tuning for a specific model family.
Curated by Param Harrison
These courses cover the engineer-focused version. No magic incantations, just patterns that generalize across GPT-4, Claude, Gemini, and local models. Skimmable, practical, and model-agnostic.
Showing 4 of 4 courses
Common questions
Yes, but less mystical than it was. Models are smarter; the patterns that still matter are structured output, eval, and task decomposition. The “prompt tricks” era is over. System design is the differentiator.
Mostly no. A well-structured prompt transfers across Claude, GPT, Gemini with minor tuning. Big differences show up in formatting (XML tags for Claude, JSON for GPT) and in how each handles tool use.
Treat user input as untrusted data, never as instructions. Use structured output, isolate roles, and validate outputs. The course covers specific injection patterns and the mitigations that actually work.
Fixed test set, measurable output property, run on every change. The LLM Observability course covers the mechanics. Don’t trust your eyeball eval on three examples. Models regress in subtle ways.