Evaluation Systems for GenAI — MangaAssist
AWS AIP-C01 Domain Coverage
Content Domain 5: Testing, Validation, and Troubleshooting Task 5.1: Implement evaluation systems for GenAI
This folder provides deep-dive implementation details for building production evaluation systems for the MangaAssist JP Manga chatbot. Every file is grounded in the MangaAssist architecture defined in 04-architecture-hld.md and 04b-architecture-lld.md.
Skill-to-File Mapping
| Skill | File | What You Learn |
|---|---|---|
| 5.1.1 FM Output Quality Assessment | 01-fm-output-quality-assessment.md | Metrics for relevance, factual accuracy, consistency, and fluency beyond traditional ML evaluation |
| 5.1.2 Model Evaluation and Optimal Configuration | 02-model-evaluation-optimal-configuration.md | Bedrock Model Evaluations, A/B testing, canary testing, cost-performance analysis, token efficiency |
| 5.1.3 User-Centered Evaluation | 03-user-centered-evaluation.md | Feedback interfaces, rating systems, annotation workflows for continuous FM improvement |
| 5.1.4 Quality Assurance Processes | 04-quality-assurance-processes.md | Continuous evaluation workflows, regression testing, automated quality gates for deployments |
| 5.1.5 Comprehensive Multi-Perspective Assessment | 05-comprehensive-assessment-multi-perspective.md | RAG evaluation, LLM-as-a-Judge, human feedback collection interfaces |
| 5.1.6 Retrieval Quality Testing | 06-retrieval-quality-testing.md | Relevance scoring, context matching verification, retrieval latency measurement |
| 5.1.7 Agent Performance Framework | 07-agent-performance-framework.md | Task completion rate, tool usage effectiveness, reasoning quality in multi-step workflows |
| 5.1.8 Reporting and Visualization Systems | 08-reporting-visualization-systems.md | Visualization tools, automated reporting, model comparison dashboards |
| 5.1.9 Deployment Validation Systems | 09-deployment-validation-systems.md | Synthetic user workflows, hallucination rate monitoring, semantic drift detection |
Cross-Cutting Files
| File | What You Learn |
|---|---|
| 10-technical-decisions-and-tradeoffs.md | Decision matrices across all 9 evaluation skills, risk-mitigation tables |
| 11-poc-implementations.md | Deployable proof-of-concept evaluation systems with production Python code |
| 12-interview-qa.md | 30+ interview questions mapped to skills 5.1.1–5.1.9, behavioral and technical |
| 13-intuition-and-strategic-direction.md | Meta-learning synthesis, evaluation maturity model, career growth signals |
Deep-Dive Scenario Folders
Each skill file has a matching subfolder containing follow-up scenarios with tiered questions (Easy → Medium → Hard → Very Hard) and answer keys:
| Skill | Scenarios |
|---|---|
| 01-fm-output-quality-assessment/ | Recommendation relevance scoring, FAQ factual accuracy validation, multi-turn consistency check, Japanese content fluency evaluation |
| 02-model-evaluation-optimal-configuration/ | Bedrock Sonnet vs Haiku evaluation, A/B testing and canary deployment, cost-performance token efficiency, latency-quality ratio analysis |
| 03-user-centered-evaluation/ | Thumbs feedback interface, response rating system, annotation workflow quality, implicit signal collection |
| 04-quality-assurance-processes/ | Continuous evaluation pipeline, regression testing model outputs, automated quality gates, golden dataset maintenance |
| 05-comprehensive-assessment-multi-perspective/ | RAG evaluation grounding check, LLM-as-Judge automated scoring, human feedback collection interface, cross-perspective agreement analysis |
| 06-retrieval-quality-testing/ | Relevance scoring with OpenSearch, context matching verification, retrieval latency measurement, embedding quality drift detection |
| 07-agent-performance-framework/ | Task completion rate measurement, tool usage effectiveness, reasoning quality in multi-step workflows, orchestrator routing accuracy |
| 08-reporting-visualization-systems/ | Evaluation dashboard design, automated reporting pipeline, model comparison visualization, stakeholder communication cadence |
| 09-deployment-validation-systems/ | Synthetic user workflow testing, hallucination rate and semantic drift, canary validation and rollback, post-deploy response consistency |
Related Content (Cross-References)
These existing files in the repository cover adjacent evaluation topics. This folder builds on them rather than duplicating:
- Model-Inference/06-model-evaluation-framework.md — Foundational evaluation architecture (golden dataset, shadow mode, canary, continuous monitoring)
- Model-Inference/07-model-evaluation-scenarios.md — Evaluation scenarios for ML and LLM models
- model_evaluation_framework_deep_dive.md — Production evaluation platform design and engineering decisions
- Cost-Optimization-Offline-Testing/01-offline-testing-strategy.md — Offline-first evaluation for cost control
- Troubleshoot-GenAI-Applications/ — Task 5.2 troubleshooting skills (complementary to this folder's Task 5.1)
- CI-CD-Pipeline-User-Stories/CD-06-configuration-prompt-pipeline.md — Prompt deployment pipeline with quality gates
How to Use This Folder
| Use Case | Start Here |
|---|---|
| AIP-C01 Exam Prep | Read skill files 01–09 in order for full Task 5.1 coverage |
| Production Work | Start with 04 (QA Processes) and 09 (Deployment Validation) for immediate operational value |
| Interview Preparation | Read 12 (Interview Q&A) first, then dive into specific skill files for depth |
| Career Growth | Read 13 (Intuition and Strategic Direction) for meta-learning synthesis |
| Scenario Practice | Pick any skill subfolder and work through the tiered questions |
File Structure Convention
Each skill file follows this template: 1. AIP-C01 Skill Mapping — Exam domain and skill reference 2. User Story — As a [role], I want to [capability], so that [business value] 3. Acceptance Criteria — Measurable, testable outcomes 4. High-Level Design — Architecture diagrams (Mermaid), decision frameworks, taxonomy tables 5. Low-Level Design — Production Python code (dataclasses, evaluators, scorers, reporters) 6. MangaAssist Scenarios — 3–4 concrete examples grounded in the chatbot architecture 7. Intuition Gained — Meta-learning and core instincts developed
Each scenario subfolder contains:
- README.md — Follow-up questions at Easy / Medium / Hard / Very Hard difficulty
- ANSWERS.md — Answer key with reasoning grounded in the parent skill file