Prompting isn’t magic. It is a skill. For developers and technical professionals, the difference between “meh” output and reliable results comes down to patterns, constraints, and iteration. In this session, you’ll learn the basics of good prompt engineering - how to set role, task, context, and output format. How to reduce ambiguity and how to iteratively refine prompts when the model fails.

We will walk through practical prompt patterns (constraint-based prompts, comparison prompts, process breakdown, fact check, and red-team/blue-team thinking) and show how to apply them to common scenarios like summarization, transforming data, generating outlines, and producing structured outputs you can use in other tools.

You will leave with a “prompt debugging” checklist and prompt examples that will improve quality, reduce hallucinations, and make AI output easier to validate and integrate into your workflow.

Key Takeaways

*How to structure prompts for predictable, reusable outputs
*Prompt patterns you can apply immediately across tasks (summarize, transform, compare, verify)
*An iterative “debugging” approach when prompts fail (tighten constraints, add context, validate)
*Techniques for improving trust: verification prompts and “fact check” patterns

Audience / Level
Developers, architects, QA, PMs; intermediate-to-advanced (assumes you’ve used AI tools before and want consistency)