OD CHAT PROMPT LAB — LOCAL PLANNING WORKSHEET

Destination: local notes. Do not paste this entire worksheet into OD.
Guide: https://duhployed.com/learn/modules/ourdream-prompt-lab.html
Examples and proposed native trials: not run. Prompting does not fine-tune the model.

1. DEFINE THE CHANGE
Character and saved revision:
Actual chat model label:
Visible settings and displayed charge:
Problem seen in an actual reply:
Exact original text:
One intended observable improvement:
Possible unwanted effect to inspect:

2. CHOOSE ONE DESTINATION AND LIFETIME
APD: durable motive, behavior, written voice.
Scenario: starting circumstances.
Greeting: actual opening message.
ED or chat Custom Instructions: one maintained location for applicable behavior.
Memory: established continuing facts in the appropriate available record.
/direct: next-reply event, if offered by the command menu.
Persona: user's selected identity.
These jobs do not establish absolute field precedence.

Chosen destination:
Persistent / current state / next reply only:
Conflicting or duplicate text to remove or reconcile:

3. WRITE THE FIELD PAYLOAD
Name the actor, concrete action, relevant condition, and stopping point.
Use only as much structure as the task needs.
Replacement payload:
[Write the actual field text here; copy only that prepared block to OD.]

4. PREPARE COMPARABLE TRIALS
Baseline card, Greeting, Persona, memory and settings saved:
How each relevant input will be restored:
Unmatched inputs or unavailable controls:
Ordinary exact test input:
Criterion written before testing:
Separate reminder/stress input, if needed:

5. RECORD EVERY ATTEMPT
Version A/B and attempt:
Status: not run / blocked / inconclusive / observed pass / observed failure
Exact submitted input and retained output:
Intended behavior observed:
Unintended drift:
Regeneration/edit/continuation and which reply was accepted:
Displayed charge, if observable:
Decision: keep / revise / restore original / investigate

Compare prompts on the same model first. Compare models as a separate change.
Model labels, costs and available controls come from the actual account.
No guessed backend identity, token allowance, hidden state, or success percentage.
Keep private account/chat information local and redact shared evidence.