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GenAI & LLM
AI Mock Interview

GenAI & LLM Mock Interview with an AI that scores you live.

Practice GenAI and LLM engineering interviews on RAG, prompt engineering, agents and production evals. The AI asks current, real-world questions, scores you live and reveals the ideal answer.

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What's Covered

Every topic a GenAI & LLM interview throws at you.

Real interview territory, not generic definitions. The AI draws on these areas and pushes into the sub-topics where you are weakest.

LLM Fundamentals
TransformersTokenizationContext WindowsTemperature/Sampling
RAG
Vector DatabasesEmbeddingsChunkingRetrievalReranking
Prompt Engineering
Few-shotChain-of-thoughtSystem PromptsStructured Output
Agents & Tools
Function CallingAgent LoopsTool UseOrchestration (LangChain)
Eval & Production
Hallucination MitigationEvalsGuardrailsCost/LatencyFine-tuning vs RAG
Sample Questions

A taste of your GenAI & LLM mock interview.

These are the kinds of scenario-based questions Joshua asks. In a live session they adapt to your answers and your target role.

1

Design a RAG system for a company knowledge base. Where does retrieval quality usually break?

2

When do you choose fine-tuning over RAG, and what does each actually cost you?

3

How do you reduce hallucinations in a production LLM feature?

4

Explain chunking strategy and how it affects retrieval quality.

5

How would you evaluate an LLM application before and after shipping?

Who It's For

Built for these GenAI & LLM roles.

AI EngineerGenAI EngineerML EngineerLLM Application Developer
How It Works

Three steps to interview-ready.

1
1
Pick your level and mode

Choose junior to principal, then text, voice, or full camera + voice simulation for your GenAI & LLM interview.

2
2
Answer one question at a time

Joshua, your AI interviewer, asks scenario-based questions, pushes for specifics, and adapts to your answers across four phases.

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3
Get scored and see ideal answers

Every answer is rated on accuracy, clarity, depth and confidence, with the ideal response revealed so you improve fast.

FAQ

GenAI & LLM mock interview questions, answered.

Is this for ML engineers or app developers?

Both. It targets engineers building LLM-powered products, focusing on RAG, prompting, agents and production concerns rather than training models from scratch.

Does it cover RAG and agents?

Yes. Retrieval-augmented generation, vector databases, function calling and agent orchestration are core topics.

Is the content current?

Yes. The track reflects how GenAI is interviewed in 2026: RAG, evals, guardrails, cost/latency and fine-tuning vs RAG trade-offs.

Practice other tracks

AWSAWSMicrosoft AzureMicrosoft AzureKubernetes & EKSKubernetes & EKSGoogle CloudGoogle CloudTerraform & IaCTerraform & IaCCI/CD PipelinesCI/CD Pipelines

Ready for your GenAI & LLM interview?

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