7 Power User Methods for ChatGPT in Business Management

As a busy business manager, using ChatGPT can significantly enhance your efficiency and decision-making. However, ChatGPT effectiveness is highly dependent on the quality of prompts it receives. Crafting precise and impactful prompts ensures that smart business managers can extract the most relevant and accurate information and assistance from AI. Here's a guide on applying six strategies from OpenAI's prompt engineering guide to improve your interactions with AI for business management tasks.

1. Write Clear Instructions

AI models thrive on clarity. The more specific and detailed your prompts, the better the AI can respond. For instance, instead of asking, "How do I get more sales for my business?" opt for more detailed queries like, "What are the steps for getting more leads and increasing conversations for my [my product or service]?" This approach reduces ambiguity and guides the AI to provide focused and relevant information. Encourage the AI to adopt a persona, such as a seasoned sales manager, to align its responses with the expertise level you seek. Remember, specifying the desired length of output can also help keep responses concise and to the point.

Good Example: "What are the steps for getting more leads and increasing conversations for my [my product or service]?"

Bad Example: "How do I get more sales for my business?"

2. Provide Reference Text

AI models can inadvertently fabricate responses, especially on complex or less common topics. To mitigate this, provide reference texts, such as industry standards or guides like ISO 9000. By instructing the AI to use these texts as a basis for its answers, you encourage more accurate and reliable responses, akin to giving a student a sheet of notes during a test.

Good Example: "Using the ISO 9000 standards as a reference, explain the process of quality management for a [your business type]"

Bad Example: "Explain how to manage quality for [my business type]."

3. Split Complex Tasks into Simpler Subtasks

Complex tasks can overwhelm or confuse AI, leading to less accurate outcomes. By breaking down a business related task into smaller, manageable pieces, you can improve the quality and reliability of AI responses. For example, instead of asking for a summary of key ideas in a 60-page market research document in one prompt, ask the AI to summarize key sections individually. This modular approach makes it easier for the AI to process and respond accurately to each component.

Good Example: "Break down the attached market research document into summarized sections covering 10 pages at a time starting with the first 10 pages only."

Bad Example: "Summarize the attached market research document."

4. Give the Model Time to Think

Like humans, AI can benefit from a moment to 'think.' Encouraging the model to consider various factors or work through a problem step-by-step can lead to more thoughtful and accurate responses. Instruct the AI to deliberate on potential strategies or solutions before providing a final answer, especially for complex business task scenarios that require deep analysis.

Good Example: "Consider all the factors that might affect delivery time for [my business service]. Then, list potential mitigation strategies for the top three risks."

Bad Example: "List risk mitigation strategies for [my business service]."

5. Use Information from External Tools

AI's capabilities can be significantly enhanced by integrating external tools. For business management, this might involve using text retrieval systems to inform the AI about the latest industry articles or code execution engines to perform accurate calculations. By offloading specific tasks to specialized tools, you combine the best of AI's analytical abilities with the precision of dedicated software, leading to more efficient and reliable outcomes.

Good Example: "Use the attached report from our inventory management system to suggest improvements to optimize inventory levels."

Bad Example: "How can we optimize our inventory levels?"

6. Provide 2 to 3 Examples

Few-shot prompting involves providing two to three examples to the AI model to demonstrate the kind of response you are looking for. This approach is particularly effective in guiding the model's responses and ensuring that the output aligns closely with your expectations. When applying few-shot prompting to business management, make sure the examples are relevant and clear.

Good Example: "Here are examples of business risks: 1. Budget overrun due to unforeseen circumstances. 2. Key team member leaving the business. Based on these, identify potential risks while operating a [business type]."

Bad Example: "What are some risks while running a [business type]?"

7. Use Self Critique To Improve Answers

Self-evaluation is a strategy where you ask the AI to assess its own responses, ensuring they meet certain criteria or quality standards. This reflective approach prompts the AI to consider the accuracy, relevance, and utility of its answers, leading to improved results. In business management, where precision and applicability are crucial, employing self-evaluation can significantly refine the AI's output. After providing an answer, instruct ChatGPT to evaluate its response based on specific criteria relevant to your business related query. This might include accuracy, completeness, adherence to business operation processes, or alignment with your business objectives. By doing so, ChatGPT is encouraged to critically assess its output, identify any potential shortcomings, and suggest improvements or alternative solutions.

Good Example: "List the potential risks in launching a new technology product and then evaluate if the list comprehensively covers technological, financial, and market-related risks. After listing the risks, please assess whether your response adequately covers all specified categories and suggest any additional risks that might have been overlooked." (can also split the evaluation as a follow-on 2nd prompt).

Bad Example: "What are the risks of a new product? Evaluate the response for completeness." Explanation: This prompt is too vague and doesn't provide specific criteria for self-evaluation. Without clear instructions or areas of focus, the AI's self-assessment might be superficial or misaligned with your needs.

Conclusion

Crafting effective prompts for AI for business management is both an art and a science. By writing clear instructions, providing reference texts, simplifying complex tasks, allowing the model time to think, utilizing external tools, and systematically testing changes, business leaders can significantly enhance the quality and utility of AI responses. The good and bad examples provided demonstrate the nuances and impact of well-constructed prompts. As you integrate these strategies into your interactions with AI, expect not only more accurate information but also insights that can drive your business towards faster growth and higher profits!