A model still gets the format wrong after examples: how do you check the examples for ambiguity?
When examples do not produce the correct format, check their consistency and coverage of realistic exceptions. Incidental example details may be learned as rules, so distinguish variable values from fixed structure.
Start troubleshooting here
When examples do not produce the correct format, check their consistency and coverage of realistic exceptions. Incidental example details may be learned as rules, so distinguish variable values from fixed structure. Verify field meaning before format rather than repeatedly copying one correct answer into more examples.
Identify example details mistaken for fixed rules
Symptoms include reusing an example's name, always returning its array length or filling unsupported fields with default content. Preserve the input and examples and mark values that should vary. If examples already mix field names or date formats, clarify the task before simply adding more of them.
Check that examples and written format rules agree
Compare field order, allowed values and missing-data handling across examples and against the written rules. The absence of errors or blanks in examples does not establish that real inputs lack them. Resolve discrepancies between requested and demonstrated formats rather than expecting the model to choose a priority.
Show variability with different lengths and missing fields
Select a few varied cases showing fixed structure and input-dependent values. Include absent information with explicit unknown-value handling. Separate examples from the material being processed so they are not treated as one source. For strict structures, validate the finished output against a schema; examples are not a substitute for program checks.
Verify structure and facts on unseen subjects
Use subjects and input lengths absent from the examples and check that names and quantities are not copied from them. Missing-field cases should remain unknown rather than inherit template values. Success means new inputs satisfy the same factual and formatting requirements, not that the model can repeat the demonstrated answer.
In-context examples do not update model weights or guarantee handling of every out-of-distribution input. Examples consume context, so retain differences relevant to the task. This page measures no model's response to example count; where hidden templates exist, inspect the application's final request to ensure the edited text actually takes effect.
Checked sources
These sources support the concepts and methods described here. Examples and test scope are labeled separately; check the version of the tools you use.