Contents
Generative AI is only as good as the control layer around it. This session explores two complementary strategies for predictable, high-quality outputs: Reverse Prompt Engineering (RPE) and Context Engineering.
RPE helps you analyze model outputs to uncover hidden instructions, constraints, and biases, turning “mystery behavior” into actionable insights.
Context Engineering goes beyond prompt tweaking to design the entire information environment: durable system instructions, structured outputs (schemas, templates), grounding with authoritative sources, workflow orchestration, and memory management.
I’ll walk through examples and share ready-to-use templates for system prompts, schema patterns, and context injection.
Takeaways
- Understand and apply the Context Engineering controls
- Use HEAT, structure, and grounding (HEAT-S/G) to evaluate AI outputs.
- Apply Reverse Prompt Engineering to analyze and improve outputs.
- Implement ready-to-use templates
Prior knowledge
a basic understanding of generative AI and prompt engineering would be beneficial but is not absolutely necessary.