AI296 – Develop, Test, and Run Granite Family LLMs with Red Hat Enterprise Linux AI — Lesson 2: Data Preparation, Prompt Design, Model Interaction, Inference, and Applications

Learning Objectives

• Configure the principal services, objects, workflows, and controls for data preparation, prompt design, and model interaction.
• Apply inference and application integration while preserving reviewable technical evidence.
• Recognize and correct the condition where a valid change produces the wrong outcome because variables, permissions, mapping, or sequencing are incorrect.

Detailed Topics

• data preparation: apply the concept to the course scenario, identify dependencies and controls, and record objective evidence of the result.
• prompt design: apply the concept to the course scenario, identify dependencies and controls, and record objective evidence of the result.
• model interaction: apply the concept to the course scenario, identify dependencies and controls, and record objective evidence of the result.
• inference: apply the concept to the course scenario, identify dependencies and controls, and record objective evidence of the result.
• application integration: apply the concept to the course scenario, identify dependencies and controls, and record objective evidence of the result.

Instructor Demonstration

The instructor demonstrates data preparation, prompt design, model interaction, inference, and applications by tracing architecture, commands, configuration, system messages, and evidence. The walkthrough tests how a valid change produces the wrong outcome because variables, permissions, mapping, or sequencing are incorrect and explains how to isolate the cause without masking related issues.

Hands-On Exercise

Implement the core workflow for Data Preparation, Prompt Design, Model Interaction, Inference, and Applications and add the verified result to the RHEL AI project, model-serving configuration, evaluation results, security checklist, and operations guide.

1. Confirm scope, versions, access, dependencies, assumptions, and acceptance criteria.
2. Perform the workflow while recording commands, configuration, output, events, and decisions.
3. Test the normal path and one course-specific exception or boundary condition.
4. Retest the correction and document rollback, ownership, and follow-up actions.

Scenario Analysis

• Identify which dependency most strongly influences data preparation in the stated scenario.
• Compare two approaches for inference and explain the security, reliability, performance, and support trade-offs.
• Decide what evidence must be retained in the RHEL AI project, model-serving configuration, evaluation results, security checklist, and operations guide before operational acceptance.

Troubleshooting Guidance

Begin with the first observable difference from the expected result. Check configuration, permissions, sequence, connectivity, and functional behavior separately, then correlate logs, events, commands, metrics, and configuration history. Treat the assumption that a valid change produces the wrong outcome because variables, permissions, mapping, or sequencing are incorrect as a hypothesis until a reproducible retest confirms the cause.

Validation Checklist

• The required configuration, permissions, sequence, connectivity, and functional behavior were checked and recorded.
• The normal path and a course-specific exception were tested.
• The RHEL AI project, model-serving configuration, evaluation results, security checklist, and operations guide links conclusions to commands, configuration, logs, metrics, or other objective evidence.
• Corrective actions, rollback, ownership, and handover notes are complete.

Lesson Outcome

Learners can apply Data Preparation, Prompt Design, Model Interaction, Inference, and Applications, explain the observed platform behavior, and support the result with a reviewable RHEL AI project, model-serving configuration, evaluation results, security checklist, and operations guide.