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Data Management Best Practices for Enhanced Prompt Design

By: SEI Team


Learn the basics about prompt engineering, and discover how to get your data management up to scratch to support your prompt engineering efforts.

Artificial intelligence (AI) is here and it’s changing how businesses operate by offering unparalleled insights, automation, and efficiency. Moving forward, it will only play an increasingly pivotal role in decision-making processes, customer interactions, product development, and more. In fact, 86% of IT leaders believe that generative AI will play a major role in their organization. However, AI systems can’t have their maximum impact without effective interactions.

That’s why prompt engineering is so vital. Without careful prompt engineering, AI systems may struggle to understand user intentions or provide relevant responses and may misinterpret prompts, leading to errors and inefficiency. Having a prompt engineer who understands both the capabilities of AI technologies and the specific requirements of the task at hand is a must. But perhaps just as important as having a good prompt engineer is having efficient data management to back it up. 

As AI technologies continue to evolve and become more integral to business operations, the role of prompt engineering and data management will only become more critical. Therefore, learning the basics about prompt engineering, and discovering how to improve your data management, is essential for leveraging the full potential of AI.

What is Prompt Engineering?

Prompt engineering is the process of creating and refining prompts for generative AI tools, such as ChatGPT or DALL-E, to achieve the best results. For example, a user might ask ChatGPT to “Draft a brief on employee engagement strategies.” However, this is a rather vague request, and ChatGPT might return a generic summary of common strategies, which may not meet the firm’s need for detailed and specific advice tailored to a particular industry. To get more tailored results, they might refine their prompt to say, “Draft a 200-word brief on innovative employee engagement strategies for the tech industry, focusing on remote work environments. Include case studies from leading tech companies, and use a professional tone suitable for a management presentation.”

This iterative process of prompt engineering can help users fine-tune requests, ensuring that AI-generated outputs meet specific criteria and preferences with greater precision and accuracy.

What is a Prompt Engineer in AI?

Prompt engineering takes time and effort — and while anyone can technically rework prompts, prompt engineers are pros at designing inputs that can effectively guide artificial intelligence responses. They can use trial and error to develop a prompt library full of scripts and templates users can customize to improve AI responses. Thanks to the hard work of prompt engineers, AI models can perform tasks better, and users will be more satisfied with the results they receive from the model. Inputs from prompt engineers can improve an AI model’s ability to create art, generate code, chat with customers, analyze, synthesize, or write text.

Where Data Management & Prompt Engineering Meet

Engaging with data management best practices is crucial for enhancing prompt engineering because the data quality directly impacts the performance and effectiveness of AI models. Ensuring that the data used for training and interaction is accurate, relevant, and well-structured is fundamental for producing reliable and meaningful prompts.

But what is data management? Simply put, it’s the process of acquiring, organizing, storing, and maintaining data to ensure it is as accurate, accessible, and usable as possible. Without proper data management, the generated prompts may lack coherence, relevance, or may even propagate biases present in the training data.

To lay a strong foundation for prompt engineering that yields more robust and reliable AI interactions, organizations need to: 

1. Ensure Data Quality and Relevance

Organizations must carefully and regularly clean and validate data to ensure it’s relevant and accurate.

Data cleaning involves removing inaccuracies, duplicates, and irrelevant entries. It can be a time-consuming process, but it’s a necessary one. Regularly reviewing datasets for repeat entries and inaccuracies can ensure the information AI models run on is more accurate, making generated responses increasingly relevant and precise. Data cleaning procedures improve the overall performance of AI systems and mitigate the risk of the solution generating inaccurate or biased information.

Similarly, implementing data validation rules can help improve the quality and integrity of your data. These rules can help you identify and fix inconsistencies and discrepancies within your datasets, improving the quality of AI-generated prompts. Plus, data validation can ensure the data meets the expected quality and format standards, boosting the effectiveness of prompt engineering and potentially leading to AI models that consistently deliver accurate and insightful responses to user prompts.

2. Structure and Organize Data Efficiently

Proper data structure and organization are also vital for prompt engineers. By organizing data into meaningful categories, you can make it faster and easier for people to train AI models and craft prompts that generate relevant responses in the future.

Meanwhile, applying metadata to your data, including the data source, the time of collection, and the data type, can provide valuable context that prompt engineers and AI models can use to refine prompts and improve response accuracy.

If your organization needs a little help, SEI can help with data governance. We can design a customized data governance plan for your organization that covers where your data is stored, who has access to your data, and how it’s secured, organized, and analyzed, resulting in improved data quality that can positively impact prompt engineering and, ultimately, the final result returned by your AI model. 

3. Diversify Your Data

It’s also a good idea to diversify your data. After all, only using data from one course to train your AI model can result in a solution with a narrow view and limited real-world applications. The AI model may regularly encounter data and situations it doesn’t know how to handle, leading to poor performance.

On the other hand, organizations can boost their AI solution’s accuracy, reliability, fairness, and effectiveness across various scenarios and users by incorporating diverse datasets. These datasets should represent many different demographics, contexts, and perspectives. When an AI system is trained on diverse datasets, it can generalize better, meaning it can return quality results in a broader range of environments and more easily adapt to new situations.

Similarly, it’s a good idea to expose your AI model to data samples representing real-world scenarios in which you anticipate your AI will operate. Representative data sampling can better prepare your AI solution and improve its ability to accurately handle a wide range of prompts.

4. Monitor and Update Data Regularly

Using a diverse data selection and properly structuring and organizing it is an excellent start, but your work doesn’t end there. You’ll also need to monitor and update your data regularly to maintain its relevance and quality over time and ensure your AI model remains effective. By continuously monitoring data for changes in relevance or quality, you can ensure your AI model reflects the current state of the market and the way users are actually using your AI solution.

You’ll also want to create feedback loops to improve your AI model’s performance. This means establishing mechanisms to efficiently integrate feedback from AI interactions into your data management and prompt design processes. You’ll need to evaluate how accurate your AI model is and make changes accordingly to improve the accuracy and effectiveness of your AI’s responses.

5. Maintain Data Security and Privacy

AI models need a lot of data to learn from — but it all comes from somewhere. That’s why organizations should:

  • Anonymize sensitive data: Not only is anonymizing personal or sensitive data before using it to train AI models the right thing to do, but it’s also the legal thing to do, as many data protection laws and regulations protect people’s privacy. However, anonymizing data won’t take away from the data’s usefulness. You can still use the anonymized real-world data to improve AI interactions.
  • Implement strict access controls: It’s also a good idea to keep a close eye on who can view and manipulate the data being fed to the AI model. By monitoring and controlling access, you can ensure that the appropriate personnel use the data responsibly.
  • Adopt Privacy-Enhancing Technologies (PETs): The phrase PETs encompasses a wide range of tools and techniques that allow organizations to use data for AI training while also protecting the users’ data and privacy. Common PETs worth considering include end-to-end encryption, differential privacy, oblivious proxies, multi-party computation, and data masking.
  • Work with a professional: Working with a consultant can also help businesses maintain data security and privacy. For example, SEI can help organizations when it comes to data privacy, whether that means helping them understand and adhere to the ever-changing regulations, analyzing and improving current data handling practices, or helping them demonstrate compliance to avoid fees and audits.

6. Collaboration Between Teams

It’s all too easy for data scientists, prompt engineers, and domain experts to conduct their work in siloed environments, each focusing solely on their specific tasks and objectives. However, taking this approach can limit the effectiveness and efficiency of AI projects.

By fostering collaboration and communication between these teams, organizations can ensure that their prompt designs take into account both the data and the specific application domain.

7. Experimentation and Testing

Organizations and their prompt engineers also need to be ready and willing to experiment and test. A/B testing various prompt structures and content can provide prompt engineers with valuable insights into how different prompt variations impact the AI model’s performance and outputs. Everything from a different prompt format to the addition of a new word can impact the AI model’s response, so testing and experimentation are a must. After determining what yields the best responses from your AI model, you can refine your data management practices and prompt design strategies.

Challenges to Keep An Eye On

AI technologies are changing — and fast — so organizations can’t let their guard down. You’ll need to think about:

Balancing Data Privacy with Utility

There’s no denying that training AI models and designing prompts using real-world data can produce accurate, relevant results. However, organizations must protect people’s privacy. Not only do individuals have their own privacy concerns, but the GDPR and CCPA regulations also impose strict data handling rules.

Organizations need to find a delicate balance between data privacy and utility. This means ensuring that all of the organization’s data handling practices are compliant with relevant data protection laws and incorporating data anonymization and pseudonymization techniques. You’ll also want to regularly train your team on data privacy best practices and ethical artificial intelligence use to ensure everyone is on the same page and informed of the latest standards.

Adapting to Rapid Changes in AI and Industry

Artificial intelligence is evolving rapidly. New models and capabilities are emerging each year, which means keeping prompt designs and data management practices up to date isn’t easy. And it’s especially difficult in industries that are also experiencing fast-paced changes.

To quickly adapt to changes in artificial intelligence or your industry as a whole, you need to foster a culture of continuous learning and innovation. Encourage your team to participate in professional development opportunities. Not only should they attend AI workshops, conferences, and classes, but they should also take advantage of similar opportunities in your specific industry.

You’ll also need to implement agile data management and prompt design processes to adapt to AI and industry changes. By embracing an approach that allows for rapid iteration, experimentation, and refinement, you can quickly make adjustments when trends evolve or you receive feedback, helping you stay ahead of the curve.

Managing Scalability and Performance

Scaling AI systems within an organization can boost productivity and lead to greater cost savings, but it also presents new challenges. As AI systems scale, maintaining performance and managing the underlying data infrastructure becomes increasingly complex and time consuming. Prompt designs and data management practices that were once effective may not be sufficient for supporting larger scales of operation.

The solution is investing in a scalable cloud-based data infrastructure that will grow to keep up with your artificial intelligence needs. Additionally, you can use data caching, load balancing, and other performance optimization techniques to help your AI systems work faster and more accurately. It’s also worth regularly reviewing and updating your data architecture and prompt engineering best practices to ensure they are optimized for your current scale and performance needs.

Improve Data Management for Better Prompt Engineering with SEI

Prompt engineering is a tricky job — and improperly managed data only makes it more challenging. By following best data management practices and keeping their data infrastructure up to date, organizations can ensure that their prompt engineers have access to the high-quality, relevant data required to create effective prompts.

By adopting best practices in data management, organizations can ensure that their prompt engineering efforts are built on a foundation of quality, security, and efficiency — but it can be difficult. That’s where SEI can help. We’re a team of seasoned consultants ready to develop high-quality solutions and strategies to meet your business’s unique needs and challenges. 

When it comes to data and analytics, we provide vendor-agnostic solutions and will recommend the best one for your unique situation. Instead of solely approaching data and analytics from a technical perspective, we view it as an opportunity for strategic problem-solving. Want to learn more?