How to Use Generative AI, Part 7: Iterate

When we think about AI and human collaboration, the process of iteration—critiquing, interacting, and iterating—is pivotal in refining and improving outputs. This loop isn't' just a mechanism for enhancing the quality of results but can also be a tool for challenging biases and building a deeper understanding between AI systems and humans. Through the continuous exchange of perspectives, this iterative process enables both AI and humans to identify weaknesses, adapt to emerging needs, and ultimately, achieve superior outcomes together.

At the heart of this iterative loop is the concept of reflective interaction, where ongoing dialogue between AI and humans allows for a dynamic reassessment of strategies and outputs. This reflective process is crucial for challenging preconceived notions and biases that may otherwise go unchecked. By openly critiquing and questioning each other's reasoning, AI and humans contribute to a culture of intellectual humility, acknowledging the limitations of their knowledge and the potential for growth and improvement.

Admittedly it's early: AI can get stuck in unproductive places and become more trouble than it's worth. But this will change and we believe that a core skill in future will be able to use AI as an iteration partner in problem solving and solution development.

AI's ability to provide explanations for its outputs encourages humans to engage in critical evaluation. This strengthens human reasoning and helps people build a healthy skepticism. This critical engagement is essential for dissecting complex problems and ensuring that decisions are not just accepted at face value but are thoroughly scrutinized and validated. As you and AI analyze data and test hypotheses together, you will enhance your own analytical thinking, grounding your conclusions in evidence and rigorous analysis.

The collaborative oversight in interpreting machine-generated insights leads to a refinement of quantitative reasoning skills. By working together, humans and AI can navigate the nuances of data interpretation, ensuring that insights are not only accurate but also meaningful and applicable to real-world scenarios. This partnership is instrumental in developing analytical abilities and strategic decision-making skills, enabling both parties to operate at new scales and with greater complexity.

The partnership between humans and AI, characterized by a loop of critique, interaction, and iteration, is more than just a method for improving outputs. It represents a fundamental shift in how we approach problem-solving and decision-making.

By leveraging the strengths of both human intuition and AI's computational power, this collaborative process opens up new avenues for innovation and understanding. It allows for the development of solutions that are not only technically sound but also deeply informed by human insight and contextual awareness. This lays the foundation for a balanced approach—getting the best of both worlds.

Good, better, best

In this case, good, better, best prompting can be you, as the user, learning and deepening your prompt at each stage. Each progressive iteration of the prompts aims to elicit more detailed, actionable insights by specifying the areas of focus, analytical depth, and strategic implications, thereby facilitating a more informed and effective decision-making process.

Conversational scenario

You are tasked to understand the reasons for a 15% drop in your company's quarterly sales of your B2B software product. Use your LLM to identify alternative causes and create a plan for evaluating these other potential causes.

Step 1: Clarify & explore: Clarify the situation and explore alternative causes.

Prompt example: I’m trying to understand the reasons for a 15% drop in our company's quarterly sales of our B2B software product. We recently increased our product prices, which might be a significant factor, but I want to explore other potential causes. What are some alternative causes?

Step 2: Explore analogies: Explore analogous situations.

Prompt example: What comparative examples can you give me for other companies like mine and the reasons for sales declines?

Step 3: Prioritize: Prioritize alternative causes to investigate.

Prompt example: Given that analysis and list of comparisons, which alternative causes do you think are most worth exploring?

Step 4: Plan: Create a plan to investigate alternative causes.

Prompt example: Can you create a plan to analyze these alternative causes to determine which, if any, are the cause of my company's sales decline?

Next: Fuse—Improve synthesis of complex and conflicting information