fix: CorrectnessEvaluator honors expected_output for reference-mode grading

[AmirF194]

Description

CorrectnessEvaluator only enters reference-mode grading when expected_assertion is set on the evaluation case. expected_output is a separate, documented field on EvaluationData ("the expected response given the input"), but nothing in correctness_evaluator.py ever reads it: _has_reference() checks expected_assertion alone, and _format_reference_prompt() interpolates only expected_assertion into the judge prompt. A case that sets expected_output without also setting expected_assertion silently falls through to _evaluate_basic()'s trajectory-only grading, which can score a response CORRECT even when it contradicts the expected output, with no warning to the caller.

Fix: _has_reference() now treats either field as a reference, and _format_reference_prompt() prefers expected_assertion when both are set (unchanged behavior for existing callers) and falls back to expected_output when only that is present.

Related Issues

Fixes #334

Documentation PR

None; docstring updated in place, no separate docs page describes this behavior.

Type of Change

Bug fix

Testing

• tests/strands_evals/evaluators/test_correctness_evaluator.py::test_has_reference_true_with_expected_output_only, test_format_reference_prompt_uses_expected_output_when_no_assertion, and test_evaluate_with_expected_output_only_uses_reference_mode fail on main and pass with this change.

• Full suite passes: hatch test tests --cover (1760 passed).

• hatch run test-lint (ruff check + mypy) is clean.

• Not checked: behavior against a live model call in reference mode; the new tests mock the judge model the same way the existing reference-mode tests do.

• I ran hatch run prepare (ran its constituent checks separately: hatch run test-lint and hatch test tests --cover, not the formatter step, to avoid reformatting unrelated pre-existing files)

Checklist

• I have read the CONTRIBUTING document
• I have reviewed and understand every line of code in this PR, including any generated by AI tools, and I can explain why it works
• My change is focused and reasonably small; I have split unrelated work into separate PRs
• I have added any necessary tests that prove my fix is effective or my feature works
• I have updated the documentation accordingly
• I have added an appropriate example to the documentation to outline the feature, or no new docs are needed
• My changes generate no new warnings
• Any dependent changes have been merged and published

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