Prompt Engineering for Software Teams

LLMs are nondeterministic by design. The same prompt, run twice, can return completely different results.

That’s not a bug—it’s a fundamental property of how these models work. And it means “just try different prompts” isn’t a real strategy.

This workshop gives your team a repeatable, engineering-based approach to getting consistent, high-quality outputs—for code generation, debugging, data extraction, documentation, and more.

What You’ll Learn

• –Apply zero-shot, one-shot, and few-shot prompting patterns to real developer tasks
• –Constrain outputs with structured formats to get parseable, machine-usable results
• –Use chain-of-thought prompting to improve LLM reasoning on complex or multi-step tasks
• –Work within context window limits and avoid the “lost in the middle” problem
• –Recognize and fix common failure modes: drift, hallucination, and format breaks
• –Use delimiters, system messages, and personas to make long prompts more reliable
• –Reason about temperature and Top P when building AI-powered features via API
• –Build a Prompt Library to capture, rate, and retest prompts as models change

Learning Objectives

• –Design zero-shot, one-shot, and few-shot prompts for the tasks your team actually ships
• –Diagnose unreliable outputs and fix them with chain-of-thought, context control, and delimiters
• –Specify and validate structured output formats for reliable, automated downstream use
• –Leave with a working Prompt Library and a personal iteration workflow you can apply immediately

Who This Is For

Software engineers and developers—junior to senior—who already use AI tools and want to stop gambling on output quality.

If your team uses Copilot, ChatGPT, Claude, or Gemini for coding, debugging, or documentation and you want results you can actually rely on, this workshop is for you.

Prerequisites

• –Comfortable running code locally
• –Basic HTML, CSS, and JavaScript (enough to tweak a small web app)
• –No ML background required
• –Access to at least one LLM tool (Copilot, ChatGPT, Claude, Gemini—free tier is fine)

Workshop Format

4 hours. Four sections, each with a short talk, hands-on exercise, and Q&A.

1. →Foundations & Baselines — What prompt engineering is and isn’t, why nondeterminism matters, writing your first zero-shot prompt
2. →Zero-shot to One-shot — When zero-shot breaks down and how one well-chosen example fixes it
3. →Few-shot + Structured Output — Covering edge cases with multiple examples; locking down output format so it’s always parseable
4. →Reasoning, Context & Your Workflow — Chain-of-thought, token limits, delimiters, personas, and building a Prompt Library you’ll actually use