Generative AI for Developers — Lesson 1: Generative AI Models, Developer Use Cases, and Architecture

Learning Objectives

• Explain and apply generative ai models, developer use cases, and architecture within Generative AI for Developers.
• Configure, validate, and troubleshoot generative ai models, developer use cases, and architecture using security, quality, governance, and operational controls.
• Validate the selected approach with documented requirements, evidence, risks, and acceptance criteria.

Detailed Topics

• Core capability: Explain and apply generative ai models, developer use cases, and architecture within Generative AI for Developers
• Implementation workflow: Configure, validate, and troubleshoot generative ai models, developer use cases, and architecture using security, quality, governance, and operational controls
• Planning: stakeholders, prerequisites, dependencies, scope, assumptions, and expected outcomes
• Security and governance: identity, permissions, data protection, compliance, accessibility, and change control
• Operations: monitoring, logs, validation, documentation, support ownership, and continuous improvement

Guided Demonstration

1. Review the requirement, users, data, dependencies, permissions, risks, and expected result.
2. Demonstrate the relevant configuration, development, analysis, administration, or operational workflow step by step.
3. Capture settings, outputs, logs, screenshots, calculations, test results, and other useful evidence.
4. Compare the observed result with acceptance criteria and explain corrective action where required.

Hands-On Activity

Complete a guided implementation scenario and preserve evidence of settings, outputs, and exceptions.

• Document assumptions, prerequisites, and the planned sequence before starting.
• Test normal, boundary, failure, security, accessibility, and operational conditions.
• Record issues, corrective actions, reusable notes, and the next improvement priority.

Scenario Analysis

• Identify the business or technical decision that must be made.
• Compare valid implementation approaches and their trade-offs.
• Consider security, privacy, accessibility, performance, cost, support, and operational impact.

Troubleshooting Focus

• Separate symptoms from causes and collect evidence before applying a fix.
• Check configuration, data, permissions, dependencies, capacity, connectivity, and recent changes.
• Validate rollback, recovery, ownership, user impact, and business continuity.

Validation Checklist

• The requirement, scope, inputs, and expected outcome are clear.
• The selected design or configuration is documented and follows approved standards.
• Normal, boundary, failure, security, accessibility, and operational conditions are reviewed.
• The result is supported by evidence and the next improvement is prioritized.

Lesson Outcome

Learners can explain and apply Generative AI Models, Developer Use Cases, and Architecture in a realistic scenario and validate the result with documented evidence.