Prompt Engineering Masterclass

Explore how large language models think by predicting the next word through tokenization, billions of parameters, and decoding. Learn how prompt engineering shapes the probability space to improve model accuracy.

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.

Demonstrate how temperature settings affect outputs: 0.1 yields deterministic, production-ready SQL; 0.5 produces balanced function documentation; 0.9 generates diverse, creative data product names.

Explore how LLMs think as next token prediction machines, shaped by prompts and controls like temperature, top p, top k, and the context window budget.

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.

Role prompting assigns the AI a persona, guiding it to review bottlenecks, root causes, and code for SQL injection, cross site scripting, and data exposure vulnerabilities.

Master chain-of-thought prompting, or crt, by learning to have the ai think step by step, show its work, analyze bottlenecks like data skew, and propose actionable patching strategies.

Illustrates a chain-of-thought approach to diagnosing a data engineering etl failure, outlining likely causes, chunked extraction with retry logic, and prevention through health checks, monitoring, and alerting.

Harness meta prompting to craft reusable AI templates for SQL tasks, incorporating role, schema, task, constraints, output format, and rules, enabling a scalable prompt library for PostgreSQL, DBT, and BigQuery.

Explore prompt scaffolding as defensive prompting, using a seven-section template—role, context, task, format, constraints, edge cases, examples, input-output pairs, and fallback—to prevent hallucinated code from sneaking in.

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.

Combine techniques such as role, CRT chain of thought, and few-shot to craft production-grade prompts. Level up by using self-consistency, multiple reasoning paths, majority vote, meta-prompting, and prompt scaffolding.

Explore the six building blocks of a perfect prompt—role, context, task, format, examples, and constraints—and learn to combine them for consistency, structure, context, engineering, and rag and hallucination prevention.

Explore context engineering and retrieval augmented generation to shape model interpretation and grounding. Use system prompts, JSON/YAML inputs, metadata, memory, and summarization with RAG to reduce production AI hallucinations.

Convert vague prompts into production-ready sql by specifying role, context, tables, and task, such as identifying customers active last quarter who haven't ordered in 90-plus days, using cte-based sql.

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.

Explore six daily ai uses for data engineers. They cover pipeline debugging with root-cause tracing, schema design, data quality rules, etl code generation, auto documentation, and performance tuning.

Debug a failing data pipeline by crafting a senior Airflow troubleshooter prompt, diagnose BigQuery extraction timeouts, implement chunked extraction with retry logic, and plan preventive steps.

Master prompts for SQL generation by specifying role, schema, task, output, and constraints; use CRT for pipeline debugging, verify with explain-analyze, and build a prompt library.

Explore the eight deadly sins of prompt engineering and learn to avoid being too vague, lack of context, not iterating, trusting blindly, wrong temperature, and no examples.

Master eight prompt engineering best practices: start simple, be specific, show, don't tell, use structure, iterate fast, match temperature to the task, and verify with chain for complex tasks.

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.

Apply the 4d framework—delegate, describe, discern, be diligent—then let structure beat cleverness by using role, context, task, format, and constraints; start simple, verify outputs, and build a prompt library daily.