Agent Application DevelopmentAccount
Knowledge catalogChoose core direction and segmented content
Practice 61FundamentalsConceptsAbout 12 minutes

Corresponding knowledge: Token accounting, context capacity, and generation headroom

What are the constraints on token, context window and output upper limit respectively? How do you leave room for the next round?

Use a concrete budget example to distinguish input capacity, generation limits, and next-turn headroom, then decide when to split, filter, or compress.

Learn corresponding knowledgeInterview questions · Answer independently →
Tokencontext windowOutput budgetTruncateevidence use

Knowledge content check2026-10-03 · Check the source of the original question2026-10-03

The knowledge corresponding to this exercise:

Context and generation budgets · Hierarchical deep learning →
Knowledge unit directory

LEARN · PRACTICE · REFLECT

Knowledge exercises·Independent answers

My notes and review ↗

Principles and Solutions have been collapsed. Explain the core mechanism, boundaries and verification methods in your own words, and then compare them.

Answers and personal notes

Each modified commit will be kept as an independent history. Your level of mastery is up to you to evaluate yourself against the standards.

Explain in your own words first

The core principles, analysis, Q&A and migration cases have been closed. When you are ready, unfold it and compare it with the content to find any omissions.

Hands-on verificationComplete on demand · Suggestions20 minutes

The teaching assumptions for this question are used: window 32000, independent maximum output 8000, complete input 23000, generation budget 6000, and safety margin 1000. First calculate the amount of new inputs, then add the 4000 token tool results, give two legal adjustment options, and explain how to check the necessary evidence and complete output for each option.

Expand acceptance requirements and checkpoints
  • Calculate 2,000 tokens of initial input headroom, 34,000 tokens of total demand after adding results, and a 2,000-token shortfall.
  • At least give two options of reducing input while retaining the generation budget, and shrinking the generation scope when the task allows. Do not treat increasing output as a universal fix.
  • The illustrated numbers are teaching assumptions, and real requests are counted by target interface.
  • The parameters are not executed when the output status is incomplete; the necessary basis is verified one by one after adding or deleting evidence.

Key inspections

  • Can distinguish between token, complete input, context window, maximum output and cost budget.
  • Input and generation margins are calculated using the given assumptions to account for the impact of the next round of additional tool results.
  • Able to distinguish between inputs that are too large, generation budget exhaustion, and network outages, and do not execute incomplete parameters.
  • Knowing the long window capacity and evidence usage effect need to be verified separately.