Practice context engineering vocabulary: context windows, context-aware prompting, compression, curation, and retrieval strategies.
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A developer says 'Context is what you put in the prompt window.' In modern LLM usage, what does the 'context window' contain?
The context window is the total input an LLM receives — it can include a system prompt, prior conversation turns, retrieved documents (RAG), tool call results, and the current user message. Everything the model 'sees' at inference time.
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Your team discusses 'context curation vs. retrieval.' What does context curation mean?
Context curation is the deliberate selection of what information to include in the prompt — choosing the most relevant, useful, and non-redundant content. Retrieval is one source; curation decides what retrieved content actually goes in.
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An engineer says 'We compress context to fit the window.' What is context compression?
Context compression involves summarizing long histories, removing redundant information, or using specialized compression models to reduce the number of tokens while retaining the information the model needs to perform the task.
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A colleague says 'We need context-aware prompting for this feature.' What does context-aware prompting mean?
Context-aware prompting means the prompt is dynamically constructed based on available context — the user's history, their current task, prior outputs, or retrieved knowledge — rather than being a static template.
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Your design doc says 'The context includes the user's history.' Why is including conversation history important for multi-turn interactions?
Including conversation history ensures the model maintains coherence across turns — it can refer back to what was discussed, avoid repeating questions, and build on earlier context rather than treating each turn as a fresh start.