Move beyond trial-and-error prompting into systematic, production-grade prompt design. Covers the advanced patterns that make AIoutput reliable, grounded, testable, and ready for agentic workflows.
Applied Technical Deep Dive
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What you´ll learn |
Grounding strategies:context injection, prompt caching, and RAG prompting: how to structure prompts so the model reasons from real data rather than training assumptions
Prompt chaining and multi-step pipelines: decompose complex tasks into isolated, focused prompts with clean handoffs —the architectural pattern behind every reliable AI workflow
Reflexion and self-evaluation: define explicit rubrics inside the prompt so the model critiques and rewrites its own output against measurable criteria
Controlled divergence and convergence: instruct the model to generate unconstrained options first, then evaluate and filter with a separate prompt —prevents self-censorship and produces more useful output than asking for "creative but practical" in one shot
LLM-as-Judge:run multiple prompt framings of the same problem, then use a second model call to score, compare, and merge outputs —a practical pattern for improving reliability in production pipelines
Pick the path that matches your team – Read more
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Target audience |
Engineers, Tech Leads, Developers
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Prerequisites |
Prompt Engineering Essentials + basic programming knowledge
