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AI & ML Interview Questions

AI, GenAI, RAG, ML, and MLOps interview questions.

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AI & ML Interview Questions

AI, GenAI, RAG, ML, and MLOps interview questions.

Each question below has its own metadata. Use tags for cross-category placement: a question can have any number of tags and categories without duplication.

Question: Design a production AI agent that can call business tools without becoming an uncontrolled automation risk.

Metadata

yaml
id: agent-production
roles:
- genai-engineer
- ai-engineer
categories:
- AI / GenAI
difficulty: Senior
type: System Design
tags:
- ai-genai
featured: false
status: published
timeMinutes: 7
relatedResources:
- label: AI Agent Production Architecture
  href: /learn/architecture/ai-agent-production-architecture

Interview Question

Design a production AI agent that can call business tools without becoming an uncontrolled automation risk.

What a Strong Answer Should Cover

  • Separate model reasoning from tools
  • Least-privilege tool access
  • Argument validation and policy checks
  • Step and cost limits
  • Observability and human escalation

Common Mistakes

  • Jumping to a technology before clarifying the problem
  • Explaining the solution without the reasoning or trade-offs
  • Omitting verification, failure handling, or prevention

Interviewer Follow-ups

  • What tools should the agent never be allowed to call?
  • How do you constrain a runaway workflow?
  • Where should a human approve actions?

What the interviewer is testing

  • Requirements and constraints
  • Architecture and trade-offs
  • Reliability, security, cost, and operations

Real-World Sample Answer

A strong real-world response should connect requirements to engineering decisions, explain why the chosen approach fits the constraints, and make failure, security, cost, observability, and verification explicit.

Question: How would you keep an AI agent from performing an unsafe tool action?

Metadata

yaml
id: agent-safety
roles:
- genai-engineer
- ai-engineer
- cloud-security
categories:
- AI / GenAI
difficulty: Senior
type: System Design
tags:
- agents
- tool calling
- guardrails
- authorization
featured: deep-dive
status: published
relatedResources:
- label: AI Agent Production Architecture
  href: /learn/architecture/ai-agent-production-architecture

Interview Question

How would you keep an AI agent from performing an unsafe tool action?

What the interviewer is testing

  • Security boundaries
  • Tool authorization
  • Safe failure

Real-World Sample Answer

I would approach “How would you keep an AI agent from performing an unsafe tool action?” by clarifying the requirements first, then using these considerations: in a real interview, i would not jump straight to a technology choice. for “how would you keep an ai agent from performing an unsafe tool action?”, i would first separate model reasoning from tool permissions. then i would apply least privilege to every tool and validate arguments and policy before execution. i would also use step, cost, and time limits. finally, i would add observability and human approval for sensitive actions. i would make the assumptions explicit and explain what evidence or production signals would make me revisit the decision.. I would state my assumptions and defend the trade-offs rather than presenting the choice as universally correct.

What a Strong Answer Should Cover

In a real interview, I would not jump straight to a technology choice. For “How would you keep an AI agent from performing an unsafe tool action?”, I would first separate model reasoning from tool permissions. Then I would apply least privilege to every tool and validate arguments and policy before execution. I would also use step, cost, and time limits. Finally, I would add observability and human approval for sensitive actions. I would make the assumptions explicit and explain what evidence or production signals would make me revisit the decision.

Common Mistakes

  • Trusting the model to self-police
  • Giving broad tool permissions
  • No action-level audit trail

Interviewer Follow-ups

  • What assumptions would you clarify before committing to the design?
  • What changes if the scale, reliability target, security requirement, or budget changes?
  • What is the biggest failure mode in your proposed approach?

Question: Which two components commonly appear in an AI agent workflow?

Metadata

yaml
id: agent-workflow
roles:
- ai-engineer
- genai-engineer
- ml-engineer
- mlops-engineer
categories:
- AI Fundamentals
difficulty: Foundation
type: Fundamentals
tags:
- AI
- fundamentals
- interview-basics
featured: false
status: published
timeMinutes: 3
options:
- A language model for reasoning
- Tools or actions for interacting with external systems
answer: An agent commonly combines a language model for reasoning with tools or actions
  that let it interact with external systems.
explanation:
- A language model for reasoning
- Tools or actions for interacting with external systems
- A requirement to avoid all external data
- A rule that every task must use the same fixed answer

Interview Question

Which two components commonly appear in an AI agent workflow?

What the interviewer is testing

  • Core AI/GenAI concept recognition
  • Ability to explain the concept accurately and simply

Real-World Sample Answer

['A language model for reasoning', 'Tools or actions for interacting with external systems', 'A requirement to avoid all external data', 'A rule that every task must use the same fixed answer']

Common Mistakes

  • Memorizing a definition without being able to explain when the concept is useful
  • Naming a technology without explaining the engineering purpose

Use the connected AI and GenAI learning resources on the site to go deeper after answering.

Question: Which statement best describes agentic AI?

Metadata

yaml
id: agentic-ai
roles:
- ai-engineer
- genai-engineer
- ml-engineer
- mlops-engineer
categories:
- AI Fundamentals
difficulty: Foundation
type: Fundamentals
tags:
- AI
- fundamentals
- interview-basics
featured: must-know
status: published
timeMinutes: 3
options:
- A system that can reason about a goal and take actions toward it
- A static document with no execution capability
- A database table containing only embeddings
- A CSS framework for building dashboards
answer: A system that can reason about a goal and take actions toward it
hint: Connect agentic AI with planning, reasoning, and action.
explanation: Agentic AI systems can reason about a goal and take actions toward it,
  often combining language models, tools, planning, and feedback loops.

Interview Question

Which statement best describes agentic AI?

What the interviewer is testing

  • Core AI/GenAI concept recognition
  • Ability to explain the concept accurately and simply

Real-World Sample Answer

Agentic AI systems can reason about a goal and take actions toward it, often combining language models, tools, planning, and feedback loops.

Common Mistakes

  • Memorizing a definition without being able to explain when the concept is useful
  • Naming a technology without explaining the engineering purpose

Use the connected AI and GenAI learning resources on the site to go deeper after answering.

Question: Which two practices improve the quality of a knowledge-based AI application?

Metadata

yaml
id: ai-quality
roles:
- ai-engineer
- genai-engineer
- ml-engineer
- mlops-engineer
categories:
- AI Fundamentals
difficulty: Foundation
type: Fundamentals
tags:
- AI
- fundamentals
- interview-basics
featured: false
status: published
timeMinutes: 3
options:
- Retrieve relevant source material
- Evaluate responses against representative tasks
- Ignore the source material after retrieval
- Always choose the largest model regardless of the task
answer:
- Retrieve relevant source material
- Evaluate responses against representative tasks
hint: One choice is about giving the model useful context; the other is about measuring
  whether the system works.
explanation: Grounding the system with relevant source material and evaluating responses
  against representative tasks are both important quality practices.

Interview Question

Which two practices improve the quality of a knowledge-based AI application?

What the interviewer is testing

  • Core AI/GenAI concept recognition
  • Ability to explain the concept accurately and simply

Real-World Sample Answer

Grounding the system with relevant source material and evaluating responses against representative tasks are both important quality practices.

Common Mistakes

  • Memorizing a definition without being able to explain when the concept is useful
  • Naming a technology without explaining the engineering purpose

Use the connected AI and GenAI learning resources on the site to go deeper after answering.

Question: Design a production RAG platform for an enterprise knowledge base with strict access controls.

Metadata

yaml
id: design-rag-platform
roles:
- ai-engineer
- genai-engineer
- cloud-architect
- backend-engineer
categories:
- AI / GenAI
difficulty: Architect
type: System Design
tags:
- RAG
- enterprise
- security
- evaluation
featured: deep-dive
status: published
relatedResources:
- label: Production RAG Platform
  href: /learn/architecture/production-rag-platform

Interview Question

Design a production RAG platform for an enterprise knowledge base with strict access controls.

What the interviewer is testing

Real-World Sample Answer

I would approach “Design a production RAG platform for an enterprise knowledge base with strict access controls.” by clarifying the requirements first, then using these considerations: in a real interview, i would not jump straight to a technology choice. for “design a production rag platform for an enterprise knowledge base with strict access controls.”, i would first design ingestion and document lifecycle. then i would separate retrieval, ranking, and context construction and enforce document-level authorization. i would also define evaluation and grounding signals. finally, i would include observability, cost controls, and fallback behavior. i would make the assumptions explicit and explain what evidence or production signals would make me revisit the decision.. I would state my assumptions and defend the trade-offs rather than presenting the choice as universally correct.

What a Strong Answer Should Cover

In a real interview, I would not jump straight to a technology choice. For “Design a production RAG platform for an enterprise knowledge base with strict access controls.”, I would first design ingestion and document lifecycle. Then I would separate retrieval, ranking, and context construction and enforce document-level authorization. I would also define evaluation and grounding signals. Finally, I would include observability, cost controls, and fallback behavior. I would make the assumptions explicit and explain what evidence or production signals would make me revisit the decision.

Common Mistakes

Interviewer Follow-ups

  • What assumptions would you clarify before committing to the design?
  • What changes if the scale, reliability target, security requirement, or budget changes?
  • What is the biggest failure mode in your proposed approach?

Question: How would you reduce the cost of a high-volume LLM application without sacrificing the required quality?

Metadata

yaml
id: llm-cost
roles:
- ai-engineer
- genai-engineer
- cloud-architect
categories:
- AI / GenAI
difficulty: Senior
type: Trade-off
tags:
- LLM
- cost
- evaluation
- model routing
featured: popular
status: published

Interview Question

How would you reduce the cost of a high-volume LLM application without sacrificing the required quality?

What the interviewer is testing

  • Quality-cost trade-offs
  • Measurement
  • Workload optimization

Real-World Sample Answer

I would approach “How would you reduce the cost of a high-volume LLM application without sacrificing the required quality?” by clarifying the requirements first, then using these considerations: in a real interview, i would not jump straight to a technology choice. for “how would you reduce the cost of a high-volume llm application without sacrificing the required quality?”, i would first define quality and latency budgets first. then i would measure cost by workload and call type and reduce unnecessary context and calls. i would also use model selection or routing where appropriate. finally, i would validate every optimization against the quality target. i would make the assumptions explicit and explain what evidence or production signals would make me revisit the decision.. I would state my assumptions and defend the trade-offs rather than presenting the choice as universally correct.

What a Strong Answer Should Cover

In a real interview, I would not jump straight to a technology choice. For “How would you reduce the cost of a high-volume LLM application without sacrificing the required quality?”, I would first define quality and latency budgets first. Then I would measure cost by workload and call type and reduce unnecessary context and calls. I would also use model selection or routing where appropriate. Finally, I would validate every optimization against the quality target. I would make the assumptions explicit and explain what evidence or production signals would make me revisit the decision.

Common Mistakes

  • Choosing the cheapest model without a quality target
  • Optimizing token count without measuring outcome
  • Ignoring caching and repeated work

Interviewer Follow-ups

  • What assumptions would you clarify before committing to the design?
  • What changes if the scale, reliability target, security requirement, or budget changes?
  • What is the biggest failure mode in your proposed approach?
  • Use the Interview Preparation learning paths and related site content to deepen any topic you could not confidently explain.

Question: A model's production quality dropped even though the training pipeline did not change. What would you investigate?

Metadata

yaml
id: ml-drift
roles:
- ml-engineer
- mlops-engineer
- ai-engineer
categories:
- ML / MLOps
difficulty: Senior
type: Troubleshooting
tags:
- drift
- ML monitoring
- features
- evaluation
featured: production
status: published
relatedResources:
- label: Production ML Platform
  href: /learn/architecture/production-ml-platform

Interview Question

A model's production quality dropped even though the training pipeline did not change. What would you investigate?

What the interviewer is testing

  • Production ML diagnosis
  • Data quality
  • Model comparison

Real-World Sample Answer

I would approach “A model's production quality dropped even though the training pipeline did not change. What would you investigate?” by clarifying the requirements first, then using these considerations: in a real interview, i would not jump straight to a technology choice. for “a model's production quality dropped even though the training pipeline did not change. what would you investigate?”, i would first check feature and data drift. then i would investigate label quality and delayed feedback and look for training-serving skew. i would also compare model and data versions. finally, i would use monitoring and rollback when the failure signal is clear. i would make the assumptions explicit and explain what evidence or production signals would make me revisit the decision.. I would state my assumptions and defend the trade-offs rather than presenting the choice as universally correct.

What a Strong Answer Should Cover

In a real interview, I would not jump straight to a technology choice. For “A model's production quality dropped even though the training pipeline did not change. What would you investigate?”, I would first check feature and data drift. Then I would investigate label quality and delayed feedback and look for training-serving skew. I would also compare model and data versions. Finally, I would use monitoring and rollback when the failure signal is clear. I would make the assumptions explicit and explain what evidence or production signals would make me revisit the decision.

Common Mistakes

  • Assuming the model is unchanged so behavior should be unchanged
  • Ignoring data pipelines
  • No rollback plan

Interviewer Follow-ups

  • What assumptions would you clarify before committing to the design?
  • What changes if the scale, reliability target, security requirement, or budget changes?
  • What is the biggest failure mode in your proposed approach?

Question: Design a production ML platform that can safely promote models from training to serving.

Metadata

yaml
id: ml-platform
roles:
- ml-engineer
categories:
- MLOps
difficulty: Senior
type: System Design
tags: []
featured: false
status: published
timeMinutes: 8
relatedResources:
- label: Production ML Platform
  href: /learn/architecture/production-ml-platform

Interview Question

Design a production ML platform that can safely promote models from training to serving.

What a Strong Answer Should Cover

  • Trace data and model artifacts
  • Automate evaluation gates
  • Use a model registry
  • Monitor production behavior
  • Support rollback

Common Mistakes

  • Jumping to a technology before clarifying the problem
  • Explaining the solution without the reasoning or trade-offs
  • Omitting verification, failure handling, or prevention

Interviewer Follow-ups

  • What makes a model eligible for promotion?
  • How would you detect a bad model after release?
  • How would you roll back safely?

What the interviewer is testing

  • Requirements and constraints
  • Architecture and trade-offs
  • Reliability, security, cost, and operations

Real-World Sample Answer

A strong real-world response should connect requirements to engineering decisions, explain why the chosen approach fits the constraints, and make failure, security, cost, observability, and verification explicit.

Question: What gates should a model pass before it is promoted to production?

Metadata

yaml
id: ml-promotion
roles:
- ml-engineer
- mlops-engineer
- ai-engineer
categories:
- ML / MLOps
difficulty: Senior
type: Architecture Decision
tags:
- MLOps
- evaluation
- model registry
- rollback
featured: deep-dive
status: published

Interview Question

What gates should a model pass before it is promoted to production?

What the interviewer is testing

  • Evaluation design
  • Artifact lineage
  • Release safety

Real-World Sample Answer

I would approach “What gates should a model pass before it is promoted to production?” by clarifying the requirements first, then using these considerations: in a real interview, i would not jump straight to a technology choice. for “what gates should a model pass before it is promoted to production?”, i would first define task-specific quality thresholds. then i would track data and model artifacts and compare against the current production model. i would also check safety, latency, and resource constraints. finally, i would define promotion and rollback conditions. i would make the assumptions explicit and explain what evidence or production signals would make me revisit the decision.. I would state my assumptions and defend the trade-offs rather than presenting the choice as universally correct.

What a Strong Answer Should Cover

In a real interview, I would not jump straight to a technology choice. For “What gates should a model pass before it is promoted to production?”, I would first define task-specific quality thresholds. Then I would track data and model artifacts and compare against the current production model. I would also check safety, latency, and resource constraints. Finally, I would define promotion and rollback conditions. I would make the assumptions explicit and explain what evidence or production signals would make me revisit the decision.

Common Mistakes

  • Using one generic accuracy metric
  • No lineage
  • Promoting based only on offline performance

Interviewer Follow-ups

  • What assumptions would you clarify before committing to the design?
  • What changes if the scale, reliability target, security requirement, or budget changes?
  • What is the biggest failure mode in your proposed approach?
  • Use the Interview Preparation learning paths and related site content to deepen any topic you could not confidently explain.

Question: Reduce the cost of a high-volume LLM application without degrading its required quality target.

Metadata

yaml
id: model-cost-optimization
roles:
- ai-engineer
- genai-engineer
categories:
- AI / GenAI
difficulty: Senior
type: System Design
tags:
- ai-genai
featured: false
status: published
timeMinutes: 6
relatedResources:
- label: GenAI Architect Interview
  href: /prepare/interviews/genai-architect-interview

Interview Question

Reduce the cost of a high-volume LLM application without degrading its required quality target.

What a Strong Answer Should Cover

  • Define quality and latency budgets
  • Measure cost by workload
  • Use routing or model selection
  • Reduce unnecessary context and calls
  • Validate changes with evaluation

Common Mistakes

  • Jumping to a technology before clarifying the problem
  • Explaining the solution without the reasoning or trade-offs
  • Omitting verification, failure handling, or prevention

Interviewer Follow-ups

  • What quality target must remain fixed?
  • Where is the largest avoidable cost?
  • How would you validate that cheaper is still good enough?

What the interviewer is testing

  • Requirements and constraints
  • Architecture and trade-offs
  • Reliability, security, cost, and operations

Real-World Sample Answer

A strong real-world response should connect requirements to engineering decisions, explain why the chosen approach fits the constraints, and make failure, security, cost, observability, and verification explicit.

Question: What is RAG primarily used for?

Metadata

yaml
id: rag-grounding
roles:
- ai-engineer
- genai-engineer
- ml-engineer
- mlops-engineer
categories:
- AI Fundamentals
difficulty: Foundation
type: Fundamentals
tags:
- AI
- fundamentals
- interview-basics
featured: must-know
status: published
timeMinutes: 3
options:
- Retrieving relevant context before generation
- Replacing every language model with a database
- Removing all external knowledge from a prompt
- Training a model only with images
answer: Retrieving relevant context before generation
hint: Think about how LLMs can use external knowledge at inference time.
explanation: Retrieval-Augmented Generation (RAG) retrieves relevant context before
  generation so an LLM can use external or current knowledge at inference time.

Interview Question

What is RAG primarily used for?

What the interviewer is testing

  • Core AI/GenAI concept recognition
  • Ability to explain the concept accurately and simply

Real-World Sample Answer

Retrieval-Augmented Generation (RAG) retrieves relevant context before generation so an LLM can use external or current knowledge at inference time.

Common Mistakes

  • Memorizing a definition without being able to explain when the concept is useful
  • Naming a technology without explaining the engineering purpose

Use the connected AI and GenAI learning resources on the site to go deeper after answering.

Question: A model gives confident answers that are not supported by retrieved documents. How would you troubleshoot it?

Metadata

yaml
id: rag-hallucination
roles:
- ai-engineer
- genai-engineer
- ml-engineer
categories:
- AI / GenAI
difficulty: Senior
type: Troubleshooting
tags:
- RAG
- hallucination
- grounding
- evaluation
featured: deep-dive
status: published
relatedResources:
- label: RAG Hallucinations
  href: /learn/ai/troubleshooting/rag-hallucinations

Interview Question

A model gives confident answers that are not supported by retrieved documents. How would you troubleshoot it?

What the interviewer is testing

  • Layered diagnosis
  • Evidence-based evaluation
  • Fallback design

Real-World Sample Answer

I would approach “A model gives confident answers that are not supported by retrieved documents. How would you troubleshoot it?” by clarifying the requirements first, then using these considerations: in a real interview, i would not jump straight to a technology choice. for “a model gives confident answers that are not supported by retrieved documents. how would you troubleshoot it?”, i would first separate retrieval quality from prompt/context construction. then i would check whether retrieved evidence actually supports the answer and evaluate model behavior on a representative set. i would also define refusal or fallback behavior. finally, i would instrument retrieval, context, and generation separately. i would make the assumptions explicit and explain what evidence or production signals would make me revisit the decision.. I would state my assumptions and defend the trade-offs rather than presenting the choice as universally correct.

What a Strong Answer Should Cover

In a real interview, I would not jump straight to a technology choice. For “A model gives confident answers that are not supported by retrieved documents. How would you troubleshoot it?”, I would first separate retrieval quality from prompt/context construction. Then I would check whether retrieved evidence actually supports the answer and evaluate model behavior on a representative set. I would also define refusal or fallback behavior. Finally, I would instrument retrieval, context, and generation separately. I would make the assumptions explicit and explain what evidence or production signals would make me revisit the decision.

Common Mistakes

  • Blaming the model immediately
  • Using only anecdotal examples
  • No measurable grounding target

Interviewer Follow-ups

  • What assumptions would you clarify before committing to the design?
  • What changes if the scale, reliability target, security requirement, or budget changes?
  • What is the biggest failure mode in your proposed approach?

Question: A RAG application has good latency but poor answer quality. How do you isolate the problem?

Metadata

yaml
id: rag-poor-retrieval
roles:
- ai-engineer
- genai-engineer
- troubleshooting
categories:
- AI / GenAI
difficulty: Professional
type: Troubleshooting
tags:
- ai-genai
featured: false
status: published
timeMinutes: 6
relatedResources:
- label: RAG
  href: /learn/ai/rag/rag
- label: RAG Hallucinations
  href: /learn/ai/troubleshooting/rag-hallucinations

Interview Question

A RAG application has good latency but poor answer quality. How do you isolate the problem?

What a Strong Answer Should Cover

  • Measure retrieval separately
  • Inspect chunking and metadata
  • Evaluate ranking and context construction
  • Test model behavior independently
  • Use a representative evaluation set

Common Mistakes

  • Jumping to a technology before clarifying the problem
  • Explaining the solution without the reasoning or trade-offs
  • Omitting verification, failure handling, or prevention

Interviewer Follow-ups

  • How would you prove retrieval is the problem?
  • Which evaluation set would you use?
  • What change would you test first?

What the interviewer is testing

  • Problem isolation and evidence-based reasoning
  • Safe mitigation and verification
  • Prevention and operational learning

Real-World Sample Answer

A strong real-world response should connect requirements to engineering decisions, explain why the chosen approach fits the constraints, and make failure, security, cost, observability, and verification explicit.

Question: Design a production RAG platform handling 10M queries per month.

Metadata

yaml
id: rag-production-10m
roles:
- ai-engineer
- genai-engineer
- system-design
categories:
- AI / GenAI
difficulty: Senior
type: System Design
tags:
- ai-genai
featured: false
status: published
timeMinutes: 8
relatedResources:
- label: Production RAG Platform
  href: /learn/architecture/production-rag-platform
- label: RAG Hallucinations
  href: /learn/ai/troubleshooting/rag-hallucinations

Interview Question

Design a production RAG platform handling 10M queries per month.

What a Strong Answer Should Cover

  • Clear retrieval and generation flow
  • Scalable ingestion and serving path
  • Security and tenant isolation
  • Cost controls and observability
  • Failure handling and reliability trade-offs

Common Mistakes

  • Jumping to a technology before clarifying the problem
  • Explaining the solution without the reasoning or trade-offs
  • Omitting verification, failure handling, or prevention

Interviewer Follow-ups

  • What would you clarify before choosing components?
  • How would you evaluate retrieval and generation separately?
  • What trade-off would you revisit at 10x traffic?

What the interviewer is testing

  • Requirements and constraints
  • Architecture and trade-offs
  • Reliability, security, cost, and operations

Real-World Sample Answer

A strong real-world response should connect requirements to engineering decisions, explain why the chosen approach fits the constraints, and make failure, security, cost, observability, and verification explicit.

Question: A RAG application has good latency but poor answer quality. How do you isolate the problem?

Metadata

yaml
id: rag-quality
roles:
- ai-engineer
- genai-engineer
- ml-engineer
- backend-engineer
categories:
- AI / GenAI
difficulty: Professional
type: Troubleshooting
tags:
- RAG
- retrieval
- evaluation
- LLM
featured: production
status: published
relatedResources:
- label: RAG
  href: /learn/ai/rag/rag
- label: RAG Hallucinations
  href: /learn/ai/troubleshooting/rag-hallucinations

Interview Question

A RAG application has good latency but poor answer quality. How do you isolate the problem?

What the interviewer is testing

  • Evaluation design
  • Retrieval isolation
  • Grounding reasoning

Real-World Sample Answer

I would approach “A RAG application has good latency but poor answer quality. How do you isolate the problem?” by clarifying the requirements first, then using these considerations: in a real interview, i would not jump straight to a technology choice. for “a rag application has good latency but poor answer quality. how do you isolate the problem?”, i would first measure retrieval separately from generation. then i would inspect chunking, metadata, ranking, and context construction and test model behavior independently. i would also use a representative evaluation set. finally, i would add grounding and fallback checks. i would make the assumptions explicit and explain what evidence or production signals would make me revisit the decision.. I would state my assumptions and defend the trade-offs rather than presenting the choice as universally correct.

What a Strong Answer Should Cover

In a real interview, I would not jump straight to a technology choice. For “A RAG application has good latency but poor answer quality. How do you isolate the problem?”, I would first measure retrieval separately from generation. Then I would inspect chunking, metadata, ranking, and context construction and test model behavior independently. I would also use a representative evaluation set. Finally, I would add grounding and fallback checks. I would make the assumptions explicit and explain what evidence or production signals would make me revisit the decision.

Common Mistakes

  • Changing models before proving the retrieval problem
  • Evaluating only final answers
  • Ignoring evaluation-set quality

Interviewer Follow-ups

  • What assumptions would you clarify before committing to the design?
  • What changes if the scale, reliability target, security requirement, or budget changes?
  • What is the biggest failure mode in your proposed approach?

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