Intelligence Engine Applications & Developer Ecosystems
Context Inflation Controls
Context inflation is the gradual bloating of prompts with history, retrieved documents, and tool results until cost and latency explode and attention dilutes. Controls keep context lean: trim, summarize, retrieve selectively, and cache stable prefixes. The result is cheaper, faster, more accurate calls without losing the information that matters.
핵심 내용
- Measure tokens per request and cost per task, not per call.
- Trim conversation history with summarization or sliding windows.
- Retrieve only the chunks relevant to the current query.
- Cache stable system prompts and prefixes across calls.
- Compress tool results before feeding them back to the model.
- Set hard token budgets per step and fail gracefully.
- Test accuracy as context shrinks to find the minimum viable size.
- Log context composition to spot inflation over time.
자주 묻는 질문
Is longer context ever worth it?
For whole-codebase or large-document tasks, yes; otherwise retrieval is cheaper.
How do I summarize safely?
Keep facts and decisions, drop filler, and verify the summary preserves key constraints.
Does caching really save money?
Yes, prefix caching can cut repeated token costs dramatically.
What is the biggest cause of inflation?
Unbounded history and over-retrieval, both easy to fix with limits.
intelligence engine applications & developer ecosystems에 대한 Kinetikatech의 지원
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