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Core
Context Compression and Prompt Compaction
When 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.
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RELATED CONCEPTS
PRACTICE THIS IN REAL QUESTIONS
LLM & GenAI FundamentalsExplain tokenization (BPE, WordPiece, SentencePiece) and why it quietly drives cost, latency, and quality.→RAG & Agent System DesignContext windows are now huge. When do you just stuff everything in context instead of building RAG?→System Design for AI in ProductionDesign a text-to-SQL feature: let users ask questions in natural language over a real database.→ML Infrastructure & GPUsHow do you autoscale AI/LLM inference workloads, and why is it harder than autoscaling web services?→LLM & GenAI FundamentalsWhat are small language models (SLMs) and reasoning models (LRMs), and when do you choose each?→RAG & Agent System DesignWhat is context engineering, and why is it considered more important than prompt engineering for agents?→
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