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LLM internals and inference

Lab: profile prompt size and answer behavior

exerciseSelf-paced

Assignment

Use a local tokenizer if available, or a deterministic token-count fixture for budget logic. Model-quality experiments can use a configured model separately.

Build sequence

  1. Implement budget accounting with reserved output and an explicit truncation policy.
  2. Create short, long, and multilingual inputs; keep character and token counts separate.
  3. Place the same evidence near the beginning and end of long contexts and record answer support.
  4. Record time to first output and total response time independently.

Acceptance checks

  • Budget checks reject requests that cannot fit mandatory instructions and the response reserve.
  • The report distinguishes measured model results from simulated timing or token fixtures.
  • A larger context is recommended only when its measured benefit justifies latency and cost.

Evidence to keep

Submit the working artifact or decision document described above, the inputs used, and the observed results for every acceptance check. Include one failed attempt and the change you made after investigating it. Label fixture-based outcomes separately from live-system measurements. These artifacts become part of your final portfolio review.

YOUR LEARNING JOURNEY

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Lab: profile prompt size and answer behavior | AI Engineer | Android Engineers