OCI AI: Generative AI Professional — Lesson 2: Prompt design, chat patterns, parameters, embeddings, semantic search, retrieval-augmented generation, and grounding data

Learning Objectives

• Translate a realistic requirement into an executable approach for Prompt design, chat patterns, parameters, embeddings, semantic search, retrieval-augmented generation, and grounding data.
• Use prompt experiment log and grounded-answer evidence to verify completeness, access, and process behaviour.
• Explain how to recover safely when retrieval returning plausible but irrelevant context for a user question occurs.

Detailed Topics

• Prompt design
• Chat patterns
• Parameters
• Embeddings
• Semantic search
• Retrieval-augmented generation
• And grounding data
• Decision ownership, prerequisites, and acceptance evidence for Prompt design, chat patterns, parameters, embeddings, semantic search, retrieval-augmented generation, and grounding data
• Relevant access, data, integration, performance, recovery, and support considerations

Instructor Demonstration

Using realistic data and role assignments, the instructor demonstrates Prompt design, chat patterns, parameters, embeddings, semantic search, retrieval-augmented generation, and grounding data. Key checkpoints are captured in prompt experiment log and grounded-answer evidence, with discussion of alternatives, side effects, and the conditions required for an acceptable result.

Hands-On Exercise

Work through a role-based scenario involving Prompt design, chat patterns, parameters, embeddings, semantic search, retrieval-augmented generation, and grounding data. Validate the expected path, test the failure case “retrieval returning plausible but irrelevant context for a user question,” choose the safest corrective action, and capture the reasoning and outcome in prompt experiment log and grounded-answer evidence.

1. Define the requirement and identify dependencies before changing the environment.
2. Create a baseline result that can be compared with later changes.
3. Reproduce the exception and test one hypothesis at a time.
4. Complete the evidence record and obtain peer or instructor review.

Scenario Analysis

• Clarify the business trigger, affected roles, required records, and acceptable result for Prompt design, chat patterns, parameters, embeddings, semantic search, retrieval-augmented generation, and grounding data.
• Compare at least two implementation or correction choices and identify which evidence would distinguish them.
• Decide how prompt experiment log and grounded-answer evidence should be reviewed, approved, retained, and handed to the next owner.

Troubleshooting Guidance

For a failure in Prompt design, chat patterns, parameters, embeddings, semantic search, retrieval-augmented generation, and grounding data, separate access, setup, data, integration, and timing causes. Compare the observed sequence with prompt experiment log and grounded-answer evidence; if “retrieval returning plausible but irrelevant context for a user question” is confirmed, make the smallest reversible correction and document why broader changes were rejected.

Validation Checklist

• Configuration and source data used for Prompt design, chat patterns, parameters, embeddings, semantic search, retrieval-augmented generation, and grounding data are versioned or otherwise traceable.
• Prompt experiment log and grounded-answer evidence shows both the baseline and the accepted result.
• Security, performance, integration, and operational side effects have been considered where relevant.
• Recovery steps and handover notes are usable by a support practitioner.

Lesson Outcome

Learners can apply Prompt design, chat patterns, parameters, embeddings, semantic search, retrieval-augmented generation, and grounding data, explain the decisions made, and defend the result with prompt experiment log and grounded-answer evidence.