reduce the accuracy requirements to pass the chat completion structured output test (#522)

i find `test_structured_output` to be flakey. it's both a functionality
and accuracy test -

```
        answer = AnswerFormat.model_validate_json(response.completion_message.content)
        assert answer.first_name == "Michael"
        assert answer.last_name == "Jordan"
        assert answer.year_of_birth == 1963
        assert answer.num_seasons_in_nba == 15
```

it's an accuracy test because it checks the value of first/last name,
birth year, and num seasons.

i find that -
- llama-3.1-8b-instruct and llama-3.2-3b-instruct pass the functionality
portion
- llama-3.2-3b-instruct consistently fails the accuracy portion
(thinking MJ was in the NBA for 14 seasons)
 - llama-3.1-8b-instruct occasionally fails the accuracy portion

suggestions (not mutually exclusive) -
1. turn the test into functionality only, skip the value checks
2. split the test into a functionality version and an xfail accuracy
version
3. add context to the prompt so the llm can answer without accessing
embedded memory

# What does this PR do?

implements option (3) by adding context to the system prompt.


## Test Plan


`pytest -s -v ... llama_stack/providers/tests/inference/ ... -k
structured_output`


## Before submitting

- [x] Ran pre-commit to handle lint / formatting issues.
- [x] Read the [contributor
guideline](https://github.com/meta-llama/llama-stack/blob/main/CONTRIBUTING.md),
      Pull Request section?
- [x] Updated relevant documentation.
- [x] Wrote necessary unit or integration tests.

llama_stack/providers/tests/inference/test_text_inference.py

@@ -211,7 +211,15 @@ class TestInference:
         response = await inference_impl.chat_completion(
             model_id=inference_model,
             messages=[
-                SystemMessage(content="You are a helpful assistant."),
+                # we include context about Michael Jordan in the prompt so that the test is
+                # focused on the funtionality of the model and not on the information embedded
+                # in the model. Llama 3.2 3B Instruct tends to think MJ played for 14 seasons.
+                SystemMessage(
+                    content=(
+                        "You are a helpful assistant.\n\n"
+                        "Michael Jordan was born in 1963. He played basketball for the Chicago Bulls for 15 seasons."
+                    )
+                ),
                 UserMessage(content="Please give me information about Michael Jordan."),
             ],
             stream=False,