Phase 1 MVP
Build career momentum with visible, repeatable progress.
Interview Prep
Turn knowledge into answer structure.
This center keeps system design, concepts, and behavioral framing in the same loop.
Roadmap
Weekly rhythm
Repetition matters more than cramming.
agents · advanced
What controls would you add before letting an agent call external tools?
1. Clarify the problem. 2. Explain tradeoffs and system boundaries. 3. Connect to production reliability. 4. Close with how you would measure success.
agents · intermediate
Where do agent loops most often go wrong in enterprise use cases?
1. Clarify the problem. 2. Explain tradeoffs and system boundaries. 3. Connect to production reliability. 4. Close with how you would measure success.
agents · advanced
When does an agent architecture add value, and when is it just complexity?
Use agents when a task genuinely needs dynamic sequencing or tool selection. Prefer deterministic workflows when the happy path is known and reliability is the priority.
agents · advanced
How would you keep an agent workflow auditable?
Use explicit states, structured tool calls, stop conditions, logs, and approval points for risky actions.
agents · intermediate
When should a workflow stay deterministic instead of becoming agentic?
Keep it deterministic when steps are known, reliability matters more than flexibility, and tool choices are stable.
backend · advanced
Design a scalable job for evaluating nightly prompt regressions across multiple datasets.
1. Clarify the problem. 2. Explain tradeoffs and system boundaries. 3. Connect to production reliability. 4. Close with how you would measure success.
backend · intermediate
How would you expose model inference through a versioned API?
1. Clarify the problem. 2. Explain tradeoffs and system boundaries. 3. Connect to production reliability. 4. Close with how you would measure success.
backend · intermediate
How would you persist experiment metadata alongside production usage data?
1. Clarify the problem. 2. Explain tradeoffs and system boundaries. 3. Connect to production reliability. 4. Close with how you would measure success.
backend · intermediate
How would you design a backend boundary between product logic and provider-specific SDK calls?
Keep provider adapters narrow, normalize payloads, and let application services depend on stable internal schemas.
backend · intermediate
How do you decide whether to persist intermediate AI artifacts?
Persist what helps replay, review, compare versions, and explain outcomes later.
behavioral · intermediate
How do you explain your transition from full-stack engineering into AI engineering?
1. Clarify the problem. 2. Explain tradeoffs and system boundaries. 3. Connect to production reliability. 4. Close with how you would measure success.
behavioral · intermediate
Describe a time you navigated an ambiguous technical frontier.
1. Clarify the problem. 2. Explain tradeoffs and system boundaries. 3. Connect to production reliability. 4. Close with how you would measure success.
behavioral · beginner
Why does your full-stack background make you effective in applied AI roles?
1. Clarify the problem. 2. Explain tradeoffs and system boundaries. 3. Connect to production reliability. 4. Close with how you would measure success.
behavioral · intermediate
How do you explain your transition from full-stack software engineering into AI engineering without sounding like you are starting over?
Frame it as an expansion of strengths: product delivery, systems thinking, API design, and ownership now applied to model-powered systems and evaluation-heavy workflows.
behavioral · intermediate
Tell me about a time you shipped an ambiguous product requirement.
Show how you created structure, aligned stakeholders, measured success, and adapted when reality changed.
behavioral · intermediate
How do you talk about an AI feature that failed its first production trial?
Focus on diagnosis quality, iteration discipline, and how the failure improved the system.
deployment · advanced
Walk through deploying a latency-sensitive inference service.
1. Clarify the problem. 2. Explain tradeoffs and system boundaries. 3. Connect to production reliability. 4. Close with how you would measure success.
deployment · intermediate
What signals tell you a model-serving architecture needs caching?
1. Clarify the problem. 2. Explain tradeoffs and system boundaries. 3. Connect to production reliability. 4. Close with how you would measure success.
deployment · advanced
What changes when an AI feature moves from a demo to a real deployment?
Cover retries, latency budgets, secrets, tracing, benchmark regressions, and human review where confidence is weak.
deployment · advanced
What belongs in an AI service health check?
Probe dependencies, provider reachability, configuration sanity, queue lag, and any signals tied to degraded user experience.
evaluation · intermediate
How do you define success for an AI feature with subjective outputs?
1. Clarify the problem. 2. Explain tradeoffs and system boundaries. 3. Connect to production reliability. 4. Close with how you would measure success.
evaluation · advanced
How would you build a benchmark suite for answer faithfulness?
1. Clarify the problem. 2. Explain tradeoffs and system boundaries. 3. Connect to production reliability. 4. Close with how you would measure success.
evaluation · intermediate
What would you put on an AI observability dashboard?
1. Clarify the problem. 2. Explain tradeoffs and system boundaries. 3. Connect to production reliability. 4. Close with how you would measure success.
evaluation · intermediate
What metrics would you put on an AI observability dashboard for a production feature?
Include answer quality, faithfulness, latency, token cost, provider failure rate, and any user-task completion signal available.
evaluation · intermediate
What makes an evaluation metric useful instead of decorative?
Useful metrics isolate a failure mode and point toward a specific next experiment or engineering fix.
evaluation · advanced
How would you debug disagreement between an automated judge and a human reviewer?
Inspect rubric ambiguity, low-quality context, edge cases, and whether the judge prompt tracks the real product goal.
llm-systems · intermediate
When would you choose RAG over fine-tuning for a product feature?
1. Clarify the problem. 2. Explain tradeoffs and system boundaries. 3. Connect to production reliability. 4. Close with how you would measure success.
llm-systems · intermediate
How do context windows influence chunking and prompt strategy?
1. Clarify the problem. 2. Explain tradeoffs and system boundaries. 3. Connect to production reliability. 4. Close with how you would measure success.
llm-systems · intermediate
How do prompt, retrieval, tools, and memory interact in an LLM application?
Explain them as distinct control surfaces, then show how poor boundaries create bugs or hidden coupling.
product · intermediate
How do you decide whether an AI feature should be fully automated or approval-driven?
1. Clarify the problem. 2. Explain tradeoffs and system boundaries. 3. Connect to production reliability. 4. Close with how you would measure success.
product · intermediate
How would you design developer onboarding around an LLM platform?
1. Clarify the problem. 2. Explain tradeoffs and system boundaries. 3. Connect to production reliability. 4. Close with how you would measure success.
python · intermediate
How would you structure a Python service that wraps an LLM provider and remains testable?
1. Clarify the problem. 2. Explain tradeoffs and system boundaries. 3. Connect to production reliability. 4. Close with how you would measure success.
python · beginner
What Python features do you rely on most when building clean AI services?
1. Clarify the problem. 2. Explain tradeoffs and system boundaries. 3. Connect to production reliability. 4. Close with how you would measure success.
python · intermediate
How do dataclasses and Pydantic serve different roles in backend design?
1. Clarify the problem. 2. Explain tradeoffs and system boundaries. 3. Connect to production reliability. 4. Close with how you would measure success.
python · intermediate
How would you structure a Python service that wraps an LLM provider and remains testable as providers change?
Clarify provider boundaries, normalize request and response models, centralize retries and timeouts, and keep business logic independent from vendor-specific SDK details.
python · beginner
What Python patterns matter most when moving from web product work into AI engineering?
Focus on data modeling, serialization, scripts, async IO, and debugging speed instead of only algorithm trivia.
python · intermediate
How would you structure evaluation scripts so they are rerunnable and trustworthy?
Emphasize stable inputs, explicit outputs, logging, CLI args, and artifact persistence.
python · beginner
How do you explain the role of Pydantic in an AI backend?
Use it at boundaries to validate inputs and outputs while keeping the middle of the system simpler.
rag · advanced
How do you debug a retrieval system that appears correct in demos but fails in production?
1. Clarify the problem. 2. Explain tradeoffs and system boundaries. 3. Connect to production reliability. 4. Close with how you would measure success.
rag · intermediate
Which retrieval metrics matter when answers need citations?
1. Clarify the problem. 2. Explain tradeoffs and system boundaries. 3. Connect to production reliability. 4. Close with how you would measure success.
rag · advanced
A RAG system works well in demos but produces weak answers in production. How do you debug it systematically?
Split the problem into ingestion, chunking, ranking, prompt assembly, and answer evaluation. Use trace data and benchmark queries to isolate the weakest layer before changing the whole pipeline.
rag · advanced
How do you choose chunking and metadata strategies for a new retrieval corpus?
Tie chunk design to user questions, citation needs, ranking signals, and future filtering requirements.
rag · intermediate
What is the difference between retrieval quality and answer quality?
Retrieval quality asks whether the right evidence was found; answer quality asks whether the final response used that evidence well.
system-design · advanced
Design an AI knowledge portal for a single-user workflow with future multi-user expansion.
1. Clarify the problem. 2. Explain tradeoffs and system boundaries. 3. Connect to production reliability. 4. Close with how you would measure success.
system-design · advanced
Design a portfolio-ready AI project that highlights production quality.
1. Clarify the problem. 2. Explain tradeoffs and system boundaries. 3. Connect to production reliability. 4. Close with how you would measure success.
system-design · advanced
Design a personal AI learning portal that can grow from one private user to a multi-user SaaS later.
Explain domain boundaries, content persistence, personalization, deployment model, and how auth and multi-tenancy could be layered in without rewriting core modules.
system-design · advanced
Design an internal assistant for a company knowledge base.
Discuss ingestion, retrieval, authorization, citations, evaluation, and how feedback improves the system over time.
system-design · advanced
How would you evolve a private single-user learning portal into a multi-user SaaS?
Separate content, user activity, and recommendation logic now so auth and tenancy can be layered in later.