context engineering
AI, ML & GenAI interview questions tagged context engineering, across every topic.
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Concepts behind "context engineering"
The curriculum that explains the ideas these questions test.
Core
Context Compression and Prompt CompactionWhen a prompt is too big, compression is the last lever you should reach for, not the first. Restructuring for a stable cached prefix is bigger and cheaper, and compaction (summarizing old turns, dropping stale tool output, reranking so you send five good chunks instead of twenty mediocre ones) covers most of the rest. Hard compression trades a measurable accuracy tax for tokens, and it can raise your bill by destroying cache hits. AI, ML, and GenAI interviews probe this because candidates reach for the clever technique before the free one.🧠 Foundations of LLMs & GenAISign in
Core
Context Engineering for AgentsContext engineering is the discipline of designing the entire information payload that enters an agent's context window each turn: system instructions, memory, retrieved data, tool definitions and results, and conversation history. Most agent failures are context failures, where the right information is missing, buried, stale, or squeezing out the rest of the budget. Applied AI interviews test it because it is the highest-leverage lever on agent reliability and cost, and it sorts people who tune prompts from people who manage state.🤖 Retrieval & AgentsSign in
Core
Multi-Agent OrchestrationWhen a task is too big or varied for a single agent, an orchestrator breaks it apart and hands subtasks to focused sub-agents, each with its own clean context and tools, then synthesizes the results. The main benefit is context isolation (each sub-agent stays focused and inside its window) alongside parallelism and specialization. The costs are coordination overhead, latency, and error propagation, so you reach for multiple agents only when the task truly needs it. Applied-AI interviews test it because multi-agent designs are common and easy to over-apply.🤖 Retrieval & AgentsSign in