amazon-chatbot-experience

Senior ML Platform / Applied AI interview pack

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Amazon Chatbot Experience — Interview Pack

MangaAssist case study

1. Problem Statement - JP Manga Store Chatbot 2. User Description — Who Uses MangaAssist?3. Use Cases - What MangaAssist Does 4. End-to-End Architecture - High-Level Design (HLD)4b. Low-Level Design (LLD) - Component Deep Dives 4c. WebSocket API - Design Space and Prototype Spec 5. Website Integration — How MangaAssist Lives on Amazon.com 6. Detailed Workflow — Step-by-Step Request Flow 7. Team Size — Building and Operating MangaAssist 8. My Role as a Senior Developer at Amazon 9. Data and Integrations — What Systems MangaAssist Connects To 10. AI / LLM Design - Intelligence Behind MangaAssist 11. Scalability and Reliability - Designing for Amazon Scale 12. Security and Privacy 13. Metrics - Measuring Success 14. MVP vs Future Versions - Phased Rollout 15. Trade-offs and Challenges 16. Final Summary amazon-chatbot-experience Test Report — MangaAssist (Streamlit + 3D + ACP)Agents — MangaAssist Architecture Guide Deep Dive: Production Model Evaluation Framework NotebookLM Topic Organization Index Skills — AWS AIP-C01 Domain 2 Inventory Sub-Agents — MangaAssist MCP Server Reference header

AI First Production Engineering

Domain1 FM Integration Data Compliance

DynamoDB

DynamoDB in MangaAssist - Project Usage and Architecture Decision DynamoDB Deep Dive - Basics, Harder Questions, Pitfalls, and Scale Notes DynamoDB + Lambda Integration — Low-Level Design DynamoDB Streams & Event-Driven Architecture — Low-Level Design DynamoDB + API Gateway + Step Functions — Orchestration LLD DynamoDB + Caching (DAX & ElastiCache Redis) — Low-Level Design DynamoDB + S3 + Kinesis — Data Pipeline, Backup & Analytics LLD DynamoDB Cross-Service Security, IAM & Observability — Low-Level Design DynamoDB Basics — Comprehensive Reference DynamoDB Scaling Scenarios — How DynamoDB Outperforms Relational and Other NoSQL Databases DynamoDB

ECS Fargate Lambda

ECS Fargate - Basics to Production AWS Lambda - Basics to Production ECS Fargate + Lambda — How They Work Together in MangaAssist ECS Fargate Lambda

Evaluation Systems GenAI

Scenario 01: Recommendation Relevance Scoring — Answers 01 recommendation relevance scoring Scenario 01: Recommendation Relevance Scoring Scenario 02: FAQ Factual Accuracy Validation — Answers 02 faq factual accuracy validation Scenario 02: FAQ Factual Accuracy Validation Scenario 03: Multi-Turn Consistency Check — Answers 03 multi turn consistency check Scenario 03: Multi-Turn Consistency Check Scenario 04: Japanese Content Fluency Evaluation — Answers 04 japanese content fluency evaluation Scenario 04: Japanese Content Fluency Evaluation 01 fm output quality assessment 01: FM Output Quality Assessment — Deep Dive Scenarios 01: FM Output Quality Assessment Framework 01 — Bedrock Model Evaluation: Sonnet vs Haiku — Answers 01 bedrock model evaluation sonnet vs haiku 01 — Bedrock Model Evaluation: Sonnet vs Haiku 02 — A/B Testing & Canary Deployment — Answers 02 ab testing canary deployment 02 — A/B Testing & Canary Deployment 03 — Cost–Performance & Token Efficiency — Answers 03 cost performance token efficiency 03 — Cost–Performance & Token Efficiency 04 — Latency–Quality Ratio Analysis — Answers 04 latency quality ratio analysis Scenario 04 — Latency–Quality Ratio Analysis 02 model evaluation optimal configuration 02 — Model Evaluation & Optimal Configuration 02: Model Evaluation and Optimal Configuration Answers – Scenario 01: Thumbs Feedback Interface 01 thumbs feedback interface Scenario 01 – Thumbs Feedback Interface Answers – Scenario 02: Response Rating System 02 response rating system Scenario 02 – Response Rating System 03 annotation workflow quality Scenario 03 – Annotation Workflow & Quality 04 implicit signal collection Scenario 04 – Implicit Signal Collection 03 user centered evaluation 03 – User-Centered Evaluation 03: User-Centered Evaluation Systems 04: Quality Assurance Processes 05: Comprehensive Assessment from Multiple Perspectives 06: Retrieval Quality Testing 07: Agent Performance Framework 08: Reporting and Visualization Systems 09: Deployment Validation Systems Evaluation Systems GenAI Evaluation Systems for GenAI — MangaAssist

Fine Tuning Foundational Models

Ground Truth Evolution

Ground-Truth Evolution — Overview The Framework — Five Axes of Ground-Truth Change GenAI Scenarios — Index GenAI Scenario 01 — Catalog Evolution / New Manga Releases GenAI Scenario 02 — Policy / T&C Document Versioning GenAI Scenario 03 — User Preference Concept Drift GenAI Scenario 04 — FM Upgrade and Judge Recalibration GenAI Scenario 05 — Localization / Multilingual Ground Truth GenAI Scenario 06 — Trending / Temporal Decay GenAI Scenario 07 — Adversarial Prompt-Injection Evolution GenAI Scenario 08 — RAG Corpus Staleness at 10× Scale GenAI Scenarios Ground Truth Evolution ML Scenarios — Index ML Scenario 01 — Recommendation Label Decay ML Scenario 02 — Sentiment Classifier Domain Shift ML Scenario 03 — Search Ranking Invalidated by UI Redesign ML Scenario 04 — Spam / Abuse Review Detection: Adversarial Label Expiry ML Scenario 05 — Demand Forecasting and Promotional Distortion ML Scenario 06 — ABSA Aspect Emergence ML Scenario 07 — Embedding Model and Category Expansion ML Scenario 08 — Cover Art Style Drift ML Scenarios

HLD Questions

HLD Deep Dive: Architecture Overview & Core Components HLD Deep Dive: Intent Classification & Orchestration HLD Deep Dive: Conversation Memory & Context Management HLD Deep Dive: RAG Pipeline & LLM Response Generation HLD Deep Dive: Recommendations, Personalization & Caching HLD Deep Dive: Scalability, Performance & Cost HLD Deep Dive: Fault Tolerance & Reliability HLD Deep Dive: Security, Safety & Guardrails HLD Deep Dive: Analytics & Observability HLD Deep Dive: Testing & Deployment Strategy HLD Deep Dive: Architect-Level Strategy & Business Alignment HLD Interview Questions - MangaAssist Chatbot Architecture HLD Questions HLD Interview Questions — Study Guide Index

Implementation Integration Domain2

LLD Questions

LLD Questions LLD Interview Questions - MangaAssist Component Deep Dives LLD ↔ Domain 2 Skills Cross-Reference Orchestrator Request Flow Intent Classification And Entity Resolution Conversation Memory And Session Storage RAG Indexing, Retrieval, And Re-Ranking Guardrails, Validation, And Safety API Contracts, Streaming, And Escalation Analytics, Observability, And Feedback Loops Scale, Failures, And Architecture Evolution topic deep dives LLD Topic Deep-Dive Packs

LLMOps

LLMOps LLMOps User Stories — Implementation Details & Code Snippets LLMOps User Stories — MangaAssist

Leadership Narrative

Leadership Narrative How I led MangaAssist past the 90% ML-failure rate Leadership Narrative — MangaAssist Story 01 — The 92% POC trap Story 02 — Why I refused "use Claude for everything" Story 03 — Three weeks of stalled embedding fine-tuning, fixed in four days Story 04 — How 0.5 became 0.7 (and why the difference mattered) Story 05 — Stopping a 4%-per-cycle accuracy bleed with a Fisher-information penalty Story 06 — How I scaled my judgment without being in every meeting stories

ML Engineer User Stories

00. Foundations & Primitives for Amazon-Scale ML Engineering on MangaAssist 02. Cross-Story ML Platform Deep Dive deep dives ML Engineer User Stories ML Engineer User Stories — MangaAssist Chatbot (Amazon-Scale)US-MLE-01: Intent Classifier Retraining Pipeline US-MLE-02: Cross-Encoder Reranker Training & Online Ranking US-MLE-03: Sentiment + ABSA (Aspect-Based Sentiment Analysis) Lifecycle US-MLE-04: Demand Forecasting Pipeline US-MLE-05: Embedding Adapter Fine-Tuning + Re-Index US-MLE-06: Recommendation Model Training + Cold-Start US-MLE-07: Spam/Abuse Classifier + Adversarial Labels US-MLE-08: Cover-Art Style Classifier + Image Monitoring

MLflow

MLflow Deep-Dive Scenarios for MangaAssist MLflow, Bedrock, and Service Integration MLflow Low-Level Implementation Guide for MangaAssist MLflow MLflow in MangaAssist

MangaAssist Architecture DeepDive

The Deep Dive: One Query Through MangaAssist 01 — The Orchestrator Agent 02 — ProductSearchAgent 03 — OrderStatusAgent 04 — RecommendationAgent 05 — MangaQAAgent 06 — Tool Dispatch & Routing 07 — Failure Handling 08 — Memory Architecture 09 — Escalation Workflow MangaAssist Architecture DeepDive MangaAssist Architecture — Deep Dive Series

MangaAssist Interview Pack

Model Inference

01. Model Inference Pipeline — Production Challenges 02. Data Scientist Collaboration — Simplifying Production Inference Together 03. Tradeoffs & Decision Frameworks — How Metrics Drove Every Choice 04. ML Metrics Taxonomy — Full Reference + Production Application 05. LLM Metrics Taxonomy — Full Reference + Production Application 06. GPU Architecture Challenges — MangaAssist 06. Model Evaluation Framework — End-to-End Quality Gates 07. Model Evaluation Scenarios — Deep Dive Q&A 08. SageMaker Endpoints, FastAPI, asyncio, and REST for Model Inference 09. SageMaker and Azure Inference APIs for LLMs and Traditional ML Models Model Inference Model Inference - Production Challenges, Metrics, and Data Science Collaboration

Modeling Math Foundations

Matrix Theory and Linear Algebra in MangaAssist Linear Regression and Generalized Models in MangaAssist Neural Network Architectures in MangaAssist Embedding and Retrieval Architectures in MangaAssist Tools and Libraries for Modeling Foundations in MangaAssist Modeling Math Foundations Mathematical Foundations of Modeling in MangaAssist

Monitoring GenAI Systems

Monitoring GenAI Systems Monitoring GenAI Systems — AWS AIP-C01 Task 4.3 Holistic Observability Architecture for GenAI Applications Skill 4.3.1 Holistic Observability Comprehensive GenAI Monitoring Architecture Skill 4.3.2 GenAI Monitoring Integrated Observability Architecture for FM Applications Skill 4.3.3 Integrated Observability Tool Performance Framework Architecture Skill 4.3.4 Tool Performance Frameworks Vector Store Operational Management Architecture Skill 4.3.5 Vector Store Operations FM-Specific Troubleshooting Framework Architecture Skill 4.3.6 FM Troubleshooting Frameworks

Offline Testing Quality Strategies

Offline Testing Types Deep Dive — GenAI Chatbot Quality Over Quantity: Testing Philosophy for GenAI Chatbots End-to-End Integration Testing Scenarios Edge Case Testing Playbook Prompt Optimization Offline Workflow Specialized Testing Strategies Interview Q&A Scenarios Offline Testing Quality Strategies Offline Testing & Quality Strategies for GenAI Chatbots

Operational Efficiency Optimization

Optimization Tradeoffs User Stories

Optimization Tradeoffs User Stories Optimization Tradeoffs User Stories - MangaAssist Chatbot US-01: The Optimization Trilemma — Decision Framework US-02: LLM Model Tiering — Quality vs Cost vs Latency US-03: Latency Budget Allocation Across the Pipeline US-04: Real-Time vs Pre-Computed Inference US-05: RAG Retrieval Depth vs Speed vs Cost US-06: Cache Aggressiveness — Freshness vs Speed vs Cost US-07: Guardrail Strictness — Safety vs Latency vs User Experience US-08: Autoscaling Strategy — Cost vs Performance Headroom US-09: Token Budget Allocation — Context Window Partitioning US-10: Unified Optimization Decision Dashboard

POC to Production War Story

01 - The POC That Fooled Us 02 - Seven Production Catastrophes 03 - How We Overcame Each Failure POC to Production War Story POC-to-Production War Story: MangaAssist

Performance Optimization User Stories

Performance Optimization User Stories PO-01: LLM Response Latency Optimization PO-02: Intent Classifier Latency Optimization PO-03: RAG Pipeline Retrieval Performance PO-04: DynamoDB Conversation Memory Read Performance PO-05: Caching Layer Performance PO-06: WebSocket Streaming Performance PO-07: Orchestrator Concurrency and Throughput PO-08: End-to-End Latency Optimization Performance Optimization User Stories - MangaAssist Chatbot

Prompt Engineering

01. Prompt Design Principles 02. Intent-Specific Prompt Patterns 03. Foundational Model Optimization Techniques 04. RAG Prompt Integration 05. Guardrails and Prompt Hardening 06. Failure Scenarios and Workarounds 07. Prompt Evaluation, Versioning, and Regression Prompt Engineering Prompt Engineering Interview Pack - Basic Questions Only Prompt Engineering Interview Pack - Basic With Hints Prompt Engineering Interview Pack - Medium Questions Only Prompt Engineering Interview Pack - Medium With Hints Prompt Engineering Interview Pack - Hard Questions Only Prompt Engineering Interview Pack - Hard With Hints Interview Prep Prompt Engineering Interview Pack Prompt Engineering for MangaAssist

RAG MCP Integration

RAG-Based MCP Integration — MangaAssist Amazon Chatbot Catalog Search MCP — Manga Title & Metadata Retrieval User Preference & Recommendation MCP Order & Inventory MCP — Real-Time Orders and Stock Review & Sentiment MCP — Community Opinion Synthesis Support & Policy MCP — FAQ, Returns, and Billing Help Trending & Discovery MCP — Real-Time Trends and New Releases Cross-Title Link MCP — Graph-Based "More Like This" Recommendations MCP Orchestration Router — How Claude Selects and Sequences Tools RAG Retrieval Pipeline Deep Dive - Shared Infrastructure MCP Server Basics and Amazon Backend Integration Complexity RAG MCP Integration

Real World Interview Questions

Associate Director / Senior Manager — Real-World Interview Questions BMS — AI Platform Engineer: Real-World Interview Questions Bms Aiplatform Real World Interview Questions MLOps Engineer — Real-World Interview Questions SDE 3 / Lead Engineer — Real-World Interview Questions

Security Privacy Guardrails

Statistical Inference

Hypothesis Testing in MangaAssist Confidence Intervals in MangaAssist T-Tests in MangaAssist Chi-Square Tests in MangaAssist Additional Statistical Tests in MangaAssist Tools and Libraries for Statistical Inference in MangaAssist Why Hypothesis Testing Matters for an MLOps Engineer Deep Dive Scenarios: How I Used Hypothesis Testing in MangaAssist Statistical Inference Statistical Inference in MangaAssist

Statistics Foundations

How Real-World ML and AI Libraries Use These Statistics Foundations Statistics Foundations Statistics Foundations for MangaAssist

Tech Stack

Detailed Technology Stack - MangaAssist Open-Source High-Performance Libraries — How I Championed OSS for MangaAssist MLflow Tracing for LLM Observability — How It Transformed Our Debugging and Monitoring Innovation Approach & Library Tradeoff Analysis Tech Stack Tech Stack, Libraries, and Engineering Innovation vLLM Game-Changer Scenarios for MangaAssist vLLM Low-Level Implementation And Critical Decisions vLLM Interview Prep Deep Dive vLLM Deployment And Infrastructure For MangaAssist vLLM Monitoring And Troubleshooting For MangaAssist vLLM Model Preparation And Quantization For MangaAssist vllm deep dive vLLM Deep Dive for MangaAssist

Troubleshoot GenAI Applications

01: Content Handling Troubleshooting 02: FM Integration Troubleshooting 03: Prompt Engineering Troubleshooting 04: Retrieval System Troubleshooting 05: Prompt Maintenance Troubleshooting 06: Technical Decisions and Tradeoffs 07: POC Implementations 08: Best Practices and Patterns 09: Debugging Scenarios and Runbooks 10: Interview Q&A — Troubleshoot GenAI Applications 11: Intuition and Strategic Direction Troubleshoot GenAI Applications Troubleshoot GenAI Applications — Task 5.2

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