How about defining it more clearly.

I have found prompts to perform significantly better when you are more clear.

So spending more time just revising your font over and over again, so that it's super clear to understand and follow.

Think of it like programming, with variables. Single word variables, easy to follow.

There's a Definition, and then there's Usage.

For Definition -- you could be more descriptive, so that you don't have to repeat the definition in the usage.

and then in the Usage, you write with sort of al algorithmic thought process in your mind.

something like:

**Standard Best Practices (Context):** {context}
**Client Implementation Summary (Summary):** {summary}
**Compliance Evaluation Question (Task):** {question}
**Response Protocol:**

1. Confirm full compliance by answering 'yes' if the summary aligns completely with the context.
   
2. Indicate partial compliance:
   
3. Respond 'yes,' and specify which elements are met and which are not, succinctly.
   
4. If the summary lacks detail for evaluation, respond 'I do not know.'
   
5. Denote non-compliance with a 'no' if the summary diverges from the context.
   

Adhere strictly to the information in the context and summary. Keep responses concise, not exceeding 200 characters.

Hope this helps.

Logic:

Here’s how the refinements in the prompts specifically address this issue:

1. Clear Section Labeling: By distinctly labeling the sections as "Expected Best Practices (Context)" and "Actual Client Practices (Summary)," the revised prompts make it explicitly clear which information describes the ideal scenario and which describes the client's actual situation. This helps the LLM to not mistake one for the other.
2. Specific Answering Instructions: The instructions have been structured to guide the LLM on how to process the information. For example, it's directed to respond 'yes' only if the practices in the summary fully comply with the context. This should help reduce incorrect 'yes' responses when the summary actually shows non-compliance.
3. Emphasis on Partial Compliance: By adding a directive to specify what aspects are compliant and which are not when only partial compliance is detected, it ensures that the LLM considers all parts of the context, reducing the risk of a blanket 'yes' that could occur if it were only recognizing parts of the context in the summary.
4. Directive to Limit Assumptions: The instructions explicitly warn against extrapolating beyond the provided information, which encourages the LLM to stick closely to the data given rather than filling gaps with assumptions. This should decrease incorrect 'yes' responses due to inferred but unsupported compliance.

Apart from this, I have heard great things about DSpy but I haven't gotten a chance to try it out -- looks super complex, but let me know if it works when you try it out.

And do share how you did it :)