6 Simple Tips For Expert-Level Prompting
Prompting Optimised For Health Professionals And Organisations
Over the last six months, I’ve gone from a good use of AI to a near expert.
I’ve consumed a lot of content about prompt optimisation, how to craft an effective inquiry and created long and highly valuable conversations with AI.
Most of this has been done with just ChatGPT (premium, so on GPT4)
Here are 6 tips that have taken me to the expert level and how they link to workflows associated with health professionals.
- Be Clear And Specific
- Use Suitable Context
- Give Effective Examples
- Tell AI What To Do And What Not To Do
- Use Tones
- Provide Details Of How The Output Should Appear
Let’s get into the details.
Be Clear And Specific
Language models, enhanced by sophisticated natural language processing (NLP) techniques, have become adept at navigating the intricacies of human language. This capability is particularly important where the precise interpretation of language can be critical.
However, it’s crucial to acknowledge that AI’s comprehension is not infallible. It cannot infer unspoken details or discern the full context without explicit input, especially in the healthcare sector where the stakes are high and the information is complex.
For healthcare professionals, this means that when interacting with AI, it’s essential to communicate in a clear and unambiguous manner. Avoid healthcare jargon, acronyms, or colloquialisms that might be commonplace in medical settings but could confuse an AI system. For instance, instead of saying “Administer 10cc of the medication,” specify “Administer 10 millilitres of [specific medication name] intravenously.”
Similarly, when using AI for decision support, specificity can greatly influence the utility and accuracy of the recommendations provided. Rather than asking for “Best practices in managing type 2 diabetes,” request “Evidence-based guidelines for the management of type 2 diabetes in patients over 65 years of age.”
In essence, the key to harnessing the full potential of AI in healthcare is to provide it with inputs that are as precise and unambiguous as possible, thereby enabling it to support healthcare professionals effectively in their mission to deliver high-quality care.
Use Suitable Context
Providing effective context to AI systems is crucial for ensuring their optimal function and relevance to specific healthcare scenarios. AI’s ability to assist healthcare professionals is greatly enhanced when it is furnished with contextual information that guides its processing and decision-making.
When leveraging AI for data analysis, context can transform raw data into actionable insights. For example, when analysing patient readmission rates, merely examining the raw numbers is insufficient. Providing AI with context, such as patient demographics, underlying health conditions, and previous treatment plans, enables a more nuanced analysis that can uncover patterns or risk factors specific to different patient groups.
For AI-driven decision support systems, the inclusion of contextual information is vital for providing relevant recommendations. For example, when an AI system suggests treatment options, knowing the context, such as the patient’s allergy history, previous reactions to medications, or genetic information, can prevent recommendations that are ineffective or harmful.
In the research domain, providing AI with a comprehensive context, including study parameters, population demographics, and relevant previous findings, can enhance its ability to identify meaningful trends, draw accurate conclusions, and suggest innovative research directions.
Give Effective Examples
Providing AI systems with effective examples can significantly improve their learning process and the accuracy of their outputs. These examples serve as benchmarks or references that help AI understand and apply complex healthcare concepts in practical scenarios.
When training AI to assist in medical diagnostics, providing a diverse range of patient cases as examples, including rare conditions, can enhance the algorithm’s ability to recognise a broad spectrum of diseases. For instance, if an AI is being trained to identify dermatological conditions from images, supplying it with a wide array of examples, covering various skin types, conditions, and stages, will improve its diagnostic accuracy and reliability.
In developing AI models to predict patient outcomes, using historical data as examples can be instrumental. For instance, providing examples of patient recovery trajectories after a specific surgery, including details on post-operative care, patient responses, and any complications, can train the AI to more accurately predict future patients’ recovery paths.
For AI tools designed to interact with patients, such as virtual health assistants, providing examples of common patient queries, concerns, and the appropriate responses can improve the AI’s communication effectiveness. For example, training the AI with a range of patient questions about medication side effects, along with expert responses, helps the tool to provide accurate and helpful information to patients.
In each scenario, giving effective examples is crucial for training AI systems to perform complex healthcare tasks accurately. These examples not only inform the AI about the variety of cases it may encounter but also teach it to apply medical knowledge in context, ultimately enhancing the quality of healthcare services and patient outcomes.
Tell AI What To Do And What Not To Do
Guiding AI with clear instructions on permissible and impermissible actions is critical for several reasons. This approach not only enhances the effectiveness and reliability of AI applications but also safeguards patient safety and privacy, ensures compliance with regulations, and upholds ethical standards.
Precise instructions help AI systems to focus their computational resources efficiently and avoid irrelevant or misleading pathways. When AI knows exactly what to focus on, it can optimise its algorithms to deliver more accurate and relevant outcomes, which is particularly vital in healthcare, where precision can significantly impact patient care and treatment decisions.
Clear boundaries prevent AI from undertaking actions that could lead to unintended consequences, such as misinterpretation of data, inappropriate patient recommendations, or breaches of privacy. By defining what AI should not do, healthcare organisations can minimise risks and ensure that AI acts within the confines of its intended function.
Explicit instructions help ensure that AI applications respect patient confidentiality and safety. For instance, instructing AI not to access or disclose certain patient information without authorisation is a crucial step in maintaining privacy and trust in healthcare settings.
The health sector is heavily regulated, and compliance with legal and ethical standards is non-negotiable. By explicitly telling AI what it is allowed and not allowed to do, healthcare organisations can better align AI applications with these standards, avoiding potential legal issues and maintaining ethical integrity.
By emphasising what AI should do and what it should avoid, healthcare organisations can harness AI’s potential while ensuring its alignment with the core values and requirements of the health sector. This careful guidance is essential for integrating AI into healthcare in a way that is effective, safe, and ethically sound.
Use Tones
The tone of AI’s responses mirrors the language and tone you use when interacting with it. But you don’t have to leave it all to chance. You can set the desired tone in the prompts you provide.
If you ask the AI model to explain something in a cheerful and simple manner, it will adapt its language to fit that request. If you prefer a more formal or technical tone for a specific task, simply mention that preference in your prompt; the model will switch gears, and don a metaphorical lab coat.
To sustain a formal tone throughout an AI discussion assign the tone at the start of the conversation and prompt the AI to maintain the formal tone unless instructed otherwise.
Provide Details Of How The Output Should Appear
Specifying how AI outputs should appear is crucial for enhancing usability, facilitating decision-making, and ensuring that information is accessible and actionable for healthcare professionals.
When AI is used to analyse and present research data, specifying the output format can greatly aid in comprehension and subsequent application. For instance, instruct AI to present epidemiological study results in a series of clear, concise graphs and tables, making it easier for researchers to identify trends and correlations.
When AI assists in resource allocation, such as in hospital supply chain management, specify that the output should include a prioritised list of items needing replenishment, along with projected demand, helping administrators make informed decisions efficiently.
For AI systems analysing healthcare facility operations, define that the output should include a dashboard view showing key performance indicators like patient wait times, staff allocation, and treatment success rates, enabling managers to quickly assess and improve hospital efficiency.
By providing detailed instructions on how AI outputs should appear, healthcare professionals can ensure that the information is presented in a manner that supports quick comprehension, effective decision-making, and efficient operation within the healthcare sector.
By clearly defining tasks, setting expectations, and specifying desired outputs, health professionals can guide AI to deliver results that are not only accurate and relevant but also aligned with the nuanced needs of healthcare. This deliberate and thoughtful approach to interaction with AI ensures that the technology acts as an effective augment to human expertise, enhancing decision-making, improving patient outcomes, and driving operational efficiency. Ultimately, the careful optimisation of prompts is fundamental to leveraging AI as a powerful tool in the ongoing pursuit of excellence in healthcare.
