planning
AI, ML & GenAI interview questions tagged planning, across every topic.
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Concepts behind "planning"
The curriculum that explains the ideas these questions test.
Core
Agent Design Patterns: ReAct, Plan-and-Execute, ReflectionThese are the named control-flow architectures for LLM agents: ReAct interleaves reasoning and actions in a tight loop, plan-and-execute breaks the task down up front and then runs the steps, and reflection adds a self-critique pass that revises output. Each strikes a different balance among latency, token cost, and resilience. Applied AI interviews test this to see whether you choose a pattern from task structure rather than falling back on one loop for everything.🤖 Retrieval & AgentsSign in
Core
Agentic and Corrective RAGStandard RAG retrieves once then generates; agentic RAG puts retrieval in a loop so the model chooses whether to retrieve, what to query, judges the results, and retrieves again until it has enough. Corrective RAG bolts on a grader that inspects retrieval quality and reacts (re-retrieve, web search, or discard) when the context is weak. AI, ML, and GenAI engineer interviews test it because complex, multi-hop questions beat single-shot RAG, and self-correcting retrieval is the remedy, paid for in extra calls and agent-reliability concerns.🤖 Retrieval & AgentsSign in