5.6 Application Evaluation and Feedback¶

Role at Stage 5: AI Applications¶

Measure the whole application through golden sets, rubrics, judges, human review, and user feedback. This part is one capability inside the stage. It should leave behind an artifact, measurements, and a short explanation of failure modes.

Explanation¶

This part has 4 sub-parts because the topic needs that many learning units to feel natural. Some stages have more parts and some have fewer; the structure follows the topic, not a fixed template.

Part Diagram¶

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flowchart LR
  P["<b>5.6</b><br/>Application Evaluation<br/>and Feedback"]
  P --> S1["<b>5.6.1</b><br/>Golden Sets and<br/>Rubrics"]
  P --> S2["<b>5.6.2</b><br/>Retrieval Evaluation"]
  P --> S3["<b>5.6.3</b><br/>Answer Evaluation"]
  P --> S4["<b>5.6.4</b><br/>Feedback Loops and<br/>Data Flywheels"]
  P --> E["<b>Exam</b><br/>Part practice"]

Sub-Parts¶

Sub-part folder	What it explains
5.6.1 Golden Sets and Rubrics	Golden Sets and Rubrics is the working skill inside Application Evaluation and Feedback that helps you build the stage artifact, An evaluated RAG or AI workflow application with documents, prompts, tests, logs, latency, cost, and failure analysis, while collecting enough evidence to trust the result.
5.6.2 Retrieval Evaluation	Retrieval Evaluation is the working skill inside Application Evaluation and Feedback that helps you build the stage artifact, An evaluated RAG or AI workflow application with documents, prompts, tests, logs, latency, cost, and failure analysis, while collecting enough evidence to trust the result.
5.6.3 Answer Evaluation	Answer Evaluation is the working skill inside Application Evaluation and Feedback that helps you build the stage artifact, An evaluated RAG or AI workflow application with documents, prompts, tests, logs, latency, cost, and failure analysis, while collecting enough evidence to trust the result.
5.6.4 Feedback Loops and Data Flywheels	Feedback Loops and Data Flywheels is the working skill inside Application Evaluation and Feedback that helps you build the stage artifact, An evaluated RAG or AI workflow application with documents, prompts, tests, logs, latency, cost, and failure analysis, while collecting enough evidence to trust the result.

What a Person Who Masters This Part Can Do¶

Explain how Application Evaluation and Feedback supports an evaluated rag or ai workflow application with documents, prompts, tests, logs, latency, cost, and failure analysis..
Build and inspect this artifact: Create a 50-question eval suite and feedback flow.
Measure progress with: Track pass rate, judge agreement, human review notes, and feedback categories.
Debug at least one failure mode before moving to the next part.

Build and Measure¶

Build: Create a 50-question eval suite and feedback flow.

Measure: Track pass rate, judge agreement, human review notes, and feedback categories.

Tests¶

Take one 30-question exam after studying this part. It opens in a new browser tab so the study page stays available.

Open Part Exam

Back to Stage¶

Return to Stage 5: AI Applications.