Prompt Engineering Masterclass

Tune llm parameters like temperature, top p, and top k to balance determinism and creativity for tasks from sql queries to brainstorming. Learn how these controls shape responses via api.

Master shot-based prompting with zero-shot, one-shot, and few-shot techniques and learn how adding examples boosts accuracy from 60-70% to 85-95%.

Explore shot-based prompting with zero-shot, one-shot, and few-shot examples, from spam classification to SQL conversion, and introduce role prompting to guide model behavior.

Explore a scaffolded prompt for data quality checks, with a senior data quality engineer specializing in financial data, half a million rows, BigQuery standard SQL, and a data quality return.

Review the perfect prompt and its building blocks—role, context, task, format, examples, constraints—and explore context engineering, grounding, citations, chain of thought, low temperature, structured output, and self-consistency; structure beats cleverness.

Recaps the pitfalls and best practices of prompt engineering, avoiding eight deadly sins like vagueness and no context, while stressing structure, simplicity, chain of thought, and thorough verification before production.

Practice capstone exercises in prompt engineering: craft a complex SQL prompt, debug a slow Spark job with role plus chain-of-thought, and build an end-to-end pipeline with code and documentation.

Explore six reusable prompt templates for prompt engineering, including SQL, debugging, code review, documentation, data quality, and architecture templates, with roles, schema, tasks, and checklists.