Code Comprehension interview questions
Understand a codebase you did not write: tracing control flow, spotting the bug, and explaining what the system does before you change it.
27 questions9 free to practice18 company-verified (Pro)
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- Semantic Search Returns Identical Results Regardless Input Query
- LLM Summarization Tool Cuts Off Mid-Sentence Completion Issue
- Vector Embedding Space Mismatch Between Query And Document Processing
- Chatbot Ignores Prompt Instructions Despite Correct Configuration Setup
- Stateless Chatbot Losing Conversation History Between Turns
- LLM Quality Scoring System Returns Perfect Scores for All Outputs
- LLM Summarizer Producing Hallucinated Content At High Failure Rate
- Agent infinite loops on tool due to missing success detection logic
- Cache overhead exceeds benefits due to poor invalidation strategy and key design
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Real Code Comprehension questions reported from interviews at 50+ AI companies, with the golden answer and full production scoring. Unlock with Pro →
- LLM Cache Bug: Same Response Returned Regardless Model Parameter
- Token Counter Mismatch Between Dashboard Estimates And Actual LLM Costs
- RAG Chatbot Fails to Answer Despite Successfully Retrieving Relevant Documents
- LLM retry logic causes infinite loops on malformed API responses
- Embedding Model Version Mismatch Between Development Production Environments
- Prompt Injection Vulnerability in Content Filter Implementation
- Conversation history manager fails to truncate old messages properly
- AI Agent Loop Termination Bug Causes Excessive API Calls
- Async batch processor silent failures with missing error handling logic
- Agent Function Calling JSON Generation Produces Malformed Output Intermittently
- Silent data drift detection in production ML pipeline accuracy collapse
- RAG evaluation metrics masking real-world answer quality failures
- Reranker degrades pipeline performance despite correct isolation testing behavior.
- Silent Data Drift: Detecting Feature Distribution Changes Without Model Updates
- Vector Store Dimension Mismatch: Ingestion and Retrieval Pipeline Synchronization
- AI Agent Prompt Injection Attack Via Support Ticket Content
- RAG Evaluation Pipeline Failing to Filter Low Quality Responses
- Agent hallucinating confident incorrect data from tool composition failures
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