Prompt Engineering Databricks AI Playground: LLM Prototyping

About this course

“This course contains the use of artificial intelligence.”

Turn Databricks AI Playground experiments into evaluated GenAI prototypes you can defend and productionize.

Getting an LLM to return one impressive answer is easy. Building a prompt that remains useful against changing inputs, enterprise data, quality requirements, and production constraints is a different problem.

This practical course gives you a repeatable workflow for designing prompts in Databricks AI Playground, evaluating outputs systematically, comparing models and parameters, connecting prototype decisions to governed data workflows, understanding usage and cost considerations, and preparing the winning experiment for production integration.

Who this course is for:

• Data engineers building GenAI features on Databricks.
• Data scientists and ML engineers adding LLM workflows to their skill set.
• AI and application engineers evaluating model endpoints.
• Analytics engineers prototyping natural-language applications.
• Technical architects and product professionals evaluating Databricks GenAI use cases.

What you will learn:

• Design reliable system and user prompts.
• Convert business requirements into measurable LLM instructions.
• Create representative evaluation datasets.
• Compare prompt and model candidates systematically.
• Test supported generation parameters through controlled experiments.
• Design prompts around enterprise data and grounding requirements.
• Recognize hallucination, instruction, and output-format failures.
• Evaluate quality alongside usage, latency, and estimated cost.
• Translate a Playground experiment into a production handoff.
• Build an employer-ready Databricks GenAI portfolio project.

Requirements:

• Access to a Databricks workspa

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