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Practice 64FundamentalsImplementationAbout 20 minutes

Corresponding knowledge: Prompt iteration, evidence support, and independent acceptance checks

How to verify that a prompt modification improves business answers without destroying the boundaries between evidence and rejection?

Differentiate structure, provenance, policy fields, and free text support with the same task set, retaining failures and comparing old and new versions.

Learn corresponding knowledgeInterview questions · Answer independently →
PromptStructured OutputEvidenceRegression

Knowledge content check2026-10-04 · Check the source of the original question2026-10-04

The knowledge corresponding to this exercise:

Prompts, structured output, and iterative acceptance checks · Hierarchical deep learning →
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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 · Suggestions30 minutes

Run prompt_iteration.py to construct incorrect free-form text in unknown references, missing exceptions, and schema-valid objects, delivering itemized acceptance and unscored explanations.

Expand acceptance requirements and checkpoints
  • Field errors, citation errors, and policy label errors are located separately.
  • The reference label does not appear in the model input.
  • Free text support and mechanical contracts are described separately.
  • Candidate sources, data and prompt versions are available for review.

Key inspections

  • Able to distinguish between prompts, Schema, factual support and execution authorization.
  • Know that reference labels cannot be mixed into model inputs.
  • Ability to explain necessary evidence recall and separate checks for correctness of answers.
  • Version comparison retains failed and unscored items.