Use AI for Innovation – Explore Tools and Functionalities in Azure OpenAI Service and Azure AI Search — Lesson 1: Azure OpenAI and AI Search Solution Concepts

Learning Objectives

• Explain models, deployments, prompts, embeddings, indexes, retrieval, grounding, and enterprise AI scenarios
• Identify users, data sources, expected outputs, risks, access, and measurable innovation outcomes
• Validate azure openai and ai search solution concepts decisions with documented requirements, test evidence, risk controls, and measurable acceptance criteria

Detailed Topics

• Explain models: Explain models, deployments, prompts, embeddings, indexes, retrieval, grounding, and enterprise AI scenarios
• Identify users: Identify users, data sources, expected outputs, risks, access, and measurable innovation outcomes
• Validate azure openai and ai search solution concepts decisions with documented requirements: Validate azure openai and ai search solution concepts decisions with documented requirements, test evidence, risk controls, and measurable acceptance criteria

Guided Demonstration

1. Review the requirement, users, data, dependencies, permissions, risks, and expected result.
2. Demonstrate the relevant design, configuration, development, analysis, administration, or support 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 any corrective action.

Hands-On Activity

Map a business question to an Azure AI solution pattern.

• Document assumptions, prerequisites, and the planned sequence before starting.
• Complete the activity and preserve evidence of normal, boundary, and failure behavior.
• 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, 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 Azure OpenAI and AI Search Solution Concepts in a realistic scenario and validate the result with documented evidence.