CypherMonkeyAI omni-channel lab

A lifelong-learner lab on AI omni-channel agents, including voice and the contact center. Free to read. Nothing sold.

Prompting and evals · L2 · 20 min

A golden set

Learn in five minutes

  • Ten to fifty checks beat one demo.
  • Each item has an input, an expected property, and a source.
  • The property can be 'refuses' or 'cites this note'.
  • Lab 03 is a local example with a fixed file.
  • Do not add items after you see the score and pretend the set was always that.

Write the set before you tune. If you add a case because the model failed it, mark that case as new so the old score stays comparable.

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Visual

T3-R4 diagram

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Five lines on paper

  1. Write five questions about a note you can open.
  2. Mark the expected answer or the expected refusal.
  3. Run them. Record misses.
  4. Change one instruction. Run the same five.

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Quiz

When do you write the set?

The set is the yardstick.

What can an expected property be?

Refusal is a valid expected result.

What does lab 03 illustrate?

The golden file is in the repo.

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Ecosystem

Regression
A check that used to pass and now fails.

Sisters: JSON schema, Rubrics, Regression tests.

The same job-shape-test card works for chat, voice, and a tool result.

The capstone reuses this card.

Config

  • Write the goal in one sentence.
  • Name the input and the allowed output.
  • Save the trace next to the result.

Troubleshooting

  • The answer looks fluent and is wrong. Check whether a source passage actually contains the claim.
  • The loop never stops. Set a step cap and stop when the tool result repeats.

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Sources and go deeper

sources.json