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Techniques for Proactive Ambiguity Resolution
When designing prompts for generative AI models, it is crucial to address ambiguity proactively to ensure clear and accurate responses. Below are some examples from different domains, each elaborated with strategies for proactive ambiguity resolution.
Examples
- Software Development:
- Prompt: “Based on the sorting algorithm we used in ‘SortFunction_LastVersion.py’, write a Python function to sort a list of numbers in ascending order. Make sure to optimize for performance. I do not want to see any use of external libraries.”
- Negative Prompt: “Avoid using any external libraries or packages.”
- Proactive Ambiguity Resolution: Providing a previous version of a sorting function helps the AI model understand the preferred coding style and logic. The instruction for performance optimization adds specificity.
- Business Analysis:
- Prompt: “Referring to last year’s sales report in ‘Sales_Report_2022.xlsx’, create a report showing the sales trends over the past year, broken down by quarter. Ensure the new report highlights any significant changes. Do not include data from discontinued product lines.”
- Negative Prompt: “Exclude any sales data related to products that we no longer offer.”
- Proactive Ambiguity Resolution: The reference to last year’s sales report provides a basis for comparison, and the instruction to highlight significant changes ensures clarity and specificity.
- Architecture (Building Design):
- Prompt: “Considering the materials listed in ‘Sustainable_Materials_Database.xlsx’, provide a list of sustainable materials suitable for constructing a residential building in a tropical climate. Ensure all materials are locally available. Do not suggest materials that are known to have long lead times.”
- Negative Prompt: “Avoid suggesting any materials that are not readily available or have long procurement times.”
- Proactive Ambiguity Resolution: The reference to a sustainable materials database ensures that the AI model has access to relevant information, and the instruction for local availability adds specificity.
- Data Analysis:
- Prompt: “Using the dataset in ‘Customer_Purchases_2023.csv’, analyze it to identify patterns in customer purchasing behavior. Focus on trends related to product categories and time of purchase. Do not include any analysis of customer personal data.”
- Negative Prompt: “Ensure that the analysis does not involve any personal or sensitive customer information.”
- Proactive Ambiguity Resolution: Providing a specific dataset ensures the AI model knows exactly what data to analyze, and the instruction to focus on certain trends adds clarity.
- Education:
- Prompt: “Referring to our biology curriculum in ‘HighSchool_Biology_Curriculum.pdf’, explain the concept of photosynthesis in simple terms for high school students. Use diagrams if necessary. Do not use complex terminology or concepts not covered in the curriculum.”
- Negative Prompt: “Avoid using any advanced terms or concepts that are beyond the high school biology curriculum.”
- Proactive Ambiguity Resolution: The reference to the specific biology curriculum ensures that the explanation is tailored to the appropriate educational level, and the instruction to use simple terms adds specificity.
- Healthcare:
- Prompt: “Based on the guidelines in ‘Chronic_Pain_Management.pdf’, provide updated guidelines for patients on managing chronic pain at home. Ensure the advice is practical and easy to follow. Do not include any medical jargon or complex medical advice.”
- Negative Prompt: “Avoid using medical terminology that might be difficult for the average patient to understand.”
- Proactive Ambiguity Resolution: Providing existing guidelines as an exemplar ensures consistency, and the instruction for practical and easy-to-follow advice adds clarity.
- Marketing:
- Prompt: “Referring to our previous successful campaign in ‘Product_Launch_2022.docx’, create a social media post to promote our new product, ensuring to highlight its unique features. Maintain a similar tone and style. Do not make any price comparisons with competitors.”
- Negative Prompt: “Avoid making direct price comparisons with other products in the market.”
- Proactive Ambiguity Resolution: The reference to a previous successful campaign provides a style guide, and the instruction to highlight unique features adds specificity.
- Environmental Science:
- Prompt: “Building on our ‘Deforestation_Report_2022.pdf’, discuss the impact of deforestation on biodiversity in the Amazon rainforest. Ensure to update any statistics and include recent studies. Do not include any outdated information.”
- Negative Prompt: “Ensure all information is current and do not use data from before 2022.”
- Proactive Ambiguity Resolution: Providing a previous report as an exemplar ensures that the AI model has a basis for the discussion, and the instruction to update statistics adds accuracy.
- Human Resources:
- Prompt: “Using the framework in ‘Employee_Onboarding_Guide.docx’, develop a training program for new employees to introduce them to our company culture. Ensure the program is engaging and interactive. Do not include any generic or outdated onboarding activities.”
- Negative Prompt: “Avoid using any cliché or outdated onboarding activities.”
- Proactive Ambiguity Resolution: The reference to an existing onboarding guide provides a structure, and the instruction for an engaging and interactive program adds specificity.
- Graphic Design:
- Prompt: “Considering our brand guidelines in ‘Brand_Guide_2023.pdf’, design a logo for a bakery, incorporating elements that reflect freshness and quality. Ensure the design aligns with our brand image. Do not use any cliché bakery symbols like wheat or a rolling pin.”
- Negative Prompt: “Avoid using overused bakery symbols in the design.”
- Proactive Ambiguity Resolution: Providing the brand guidelines ensures that the design aligns with the company’s image, and the instruction to reflect freshness and quality adds specificity.
- Enterprise Architecture:
- Prompt: “Based on our previous strategy document ‘Enterprise_Strategy_2018.pdf’, develop a comprehensive strategy to align our IT infrastructure with the business goals for the next five years. I do not want a generic template; I need specific actions and milestones.”
- Negative Prompt: “Avoid providing a generic template.”
- Proactive Ambiguity Resolution: The inclusion of a previous strategy document as an exemplar helps the AI model understand the company’s past approaches and tailor the new strategy accordingly. The explicit mention of needing specific actions and milestones helps in resolving ambiguity by clarifying what is expected in the response.
- Business Architecture:
- Prompt: “Referencing our process documentation in ‘Business_Processes_2022.docx’, create a visual representation of our business processes, highlighting areas for improvement and innovation. Please do not include outdated processes that were phased out last year.”
- Negative Prompt: “Do not include any processes that were removed or updated last year.”
- Proactive Ambiguity Resolution: Providing a specific document for reference ensures that the AI model has a clear starting point and understands the current state of business processes. The instruction to not include outdated processes adds clarity and helps in tailoring the response.
- Technical Architecture:
- Prompt: “Using the software inventory list in ‘Software_Inventory_2023.xlsx’, evaluate our current software stack and provide recommendations for modernizing our technology to improve performance. Exclude any recommendations for software that we have tried and rejected in the past.”
- Negative Prompt: “Do not recommend any software solutions that we have previously tried and decided against.”
- Proactive Ambiguity Resolution: The inclusion of a software inventory list helps the AI model understand the current state of the software stack, and the instruction to exclude past rejections adds specificity, ensuring a tailored and relevant response.
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Information or Data Architecture:
- Prompt: “Building on our ‘Data_Quality_Report_2022.pdf’, assess our data management practices and suggest a plan to ensure data quality and compliance with industry standards. Ensure that the plan addresses the specific issues mentioned in the report.”
- Negative Prompt: “Do not suggest generic data management practices; focus on addressing the issues mentioned in the provided report.”
- Proactive Ambiguity Resolution: Providing a data quality report as an exemplar ensures that the AI model has context on existing issues, and the instruction to address specific issues in the plan adds clarity and specificity.
- Integration Architecture:
- Prompt: “Considering the integration challenges listed in ‘Integration_Issues_2023.docx’, design a solution to integrate our various software systems, ensuring seamless data flow and minimizing data silos. The solution should specifically address the challenges mentioned in the document.”
- Negative Prompt: “Do not suggest solutions that do not directly address the integration challenges listed in the provided document.”
- Proactive Ambiguity Resolution: The inclusion of a document listing integration challenges ensures that the AI model understands the specific issues at hand, and the instruction to address these challenges in the solution adds specificity and clarity.
- Deployment Architecture:
- Prompt: “Referencing our previous deployment plan in ‘Deployment_Plan_2022.pdf’, create a deployment plan for our new application, ensuring high availability and scalability to handle peak user loads. The new plan should improve upon the previous one and address any shortcomings.”
- Negative Prompt: “Do not replicate the issues from the previous deployment plan; focus on improvements and addressing shortcomings.”
- Proactive Ambiguity Resolution: Providing a previous deployment plan as an exemplar gives the AI model a basis for improvement, and the instruction to address any shortcomings in the new plan ensures specificity and relevance.
Conclusion
In each example, the inclusion of exemplars and specific instructions helps in proactively resolving ambiguity, ensuring that the AI model generates responses that are tailored, relevant, and meet the user’s expectations. The negative prompts add an additional layer of specificity by explicitly stating what the user does not want to see in the response. In each example, the strategies for proactive ambiguity resolution include providing clear instructions, context, and goals, ensuring conciseness and specificity, addressing the persona directly, and using an appropriate tone and format. These strategies help in making the content accessible, helpful, and directly addressing the audience’s needs.
Pro Tip
When creating prompts for AI, always be clear and specific about what you want. Think about who will use your prompt – like students or professionals in different fields – and make sure your instructions are easy for them to understand. This way, you help the AI give you the exact answers or solutions you’re looking for. Remember, the clearer your prompt, the better the AI’s response!
FAQs
- What is proactive ambiguity resolution in prompt design?
It’s about making sure your instructions to an AI are very clear so it understands exactly what you want.
- Why is it important for prompt design?
It helps the AI give you the right answers or solutions, saving time and avoiding confusion.
- How can I make my prompts clear for AI?
Use simple, direct language and be specific about what you need. Avoid vague or general terms.
- Can you give an example of a good prompt?
Sure! A good prompt might be, “List the steps to make a simple cake,” instead of just saying, “Tell me about cake.”
- What should I avoid in prompt design?
Stay away from complicated words, long sentences, and unclear instructions.
- Is it different for students and professionals?
Not really. Whether you’re a student or a professional, clear and simple prompts work best for everyone.
- How does this help in fields like business analysis or architecture?
Clear prompts help get precise information or designs, which is very important in these fields.
- What if I’m not sure about my prompt?
Try to break down what you need into smaller, clearer parts and build your prompt from there.
- Does proactive ambiguity resolution help in learning?
Yes, it helps students get clearer answers, which can make learning easier and more effective.
- Can this approach improve teamwork?
Absolutely! Clear prompts lead to better understanding and cooperation in a team.
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