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A data analytics company is building an AI agent that generates SQL queries from natural language. Each request includes: database schema context (40K), 3 few-shot examples (15K), the user's question (2K), and metadata (1K). Total budget is 200K. The architect wants to increase examples to 10 to cover more patterns. An engineer advises against this, arguing the largest share should go to a different section. Which section should receive the largest allocation?

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intermediate 20 min

Token Budgeting

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An edtech platform is building a study assistant that needs to reference the full content of 20 textbooks (approximately 5M tokens total). The 200K context window is far too small. The team considers upgrading to an enterprise plan, compressing with gzip, or splitting into 25 parallel sub-requests. Which approach correctly solves the context window limitation?

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A customer support startup is evaluating token cost over a 50-turn conversation. Raw history totals 400K tokens, but the team uses hierarchical summarization. After 50 turns, each request includes: a 5K system prompt, the current user message (2K), and a summary of past turns (15K). The CTO wants the total cost. What is the correct way to calculate the effective token cost?

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