Prompt Engineering Fundamentals
Systematic techniques for reliable, high-quality AI outputs
Session Topics
Elements of Effective Prompts
Context, task, format, tone, and constraints — the five ingredients of a prompt that reliably produces professional output.
Role-Based Prompting
Assigning roles to shape language, depth, and perspective. Why 'You are a senior Python developer' works and 'Write code' doesn't.
Few-Shot Learning
Using 2–5 in-prompt examples to dramatically improve performance without fine-tuning. When to use zero-shot vs few-shot.
Chain-of-Thought Prompting
Asking the model to reason step-by-step before answering. Essential for math, logic, and multi-stage decisions.
Prompt Iteration Strategy
Moving from a rough prompt to a production-ready one — systematic testing, version control, and regression checking.
Craft Your Production-Ready Resume Prompt
Systematically engineer a prompt using Session 3 techniques. Move from your Session 1 baseline to a production-ready prompt that reliably generates high-quality, structured output for multiple fictional student profiles.
Compare two GenAI outputs for the same task and justify which is better based on structure, accuracy, and usability.
Key Takeaways
- Bad prompts produce bad output — even from excellent models
- Role + context + format specification is the baseline for professional prompts
- Few-shot examples often outperform lengthy instructions
- Chain-of-thought dramatically improves accuracy on reasoning tasks