Based on prior knowledge, high levels of glucose are often associated with diabetes. Therefore, individuals with diabetes ('yes' class) are expected to have higher values of glucose compared to those without diabetes ('no' class).

To generate the dictionary, we need to analyze the feature's relationship with the target variable. We can start by examining the distribution of glucose values for each target class.

Here is a dictionary with the required information:

```json
{
	"yes": [185.0, 198.0, 220.0, 267.0, 303.0],
	"no": [80.0, 92.0, 102.0, 115.0, 120.0]
}
```

The 'yes' class includes five typical glucose values: 185.0, 198.0, 220.0, 267.0, and 303.0. These higher glucose values indicate individuals with diabetes.

The 'no' class includes five typical glucose values: 80.0, 92.0, 102.0, 115.0, and 120.0. These lower glucose values indicate individuals without diabetes.

Please note that these values are just examples and should be interpreted in the context of the dataset and its specific features.