AI for Critical Thinkers

Chapter 4 of Mostly Harmless AI

Photo by Rick J. Brown on Unsplash

This article is based on Chapter 4 of my upcoming book Mostly Harmless AI.

Having journeyed through the foundations of artificial intelligence—its history, its mechanics, and its limitations—we arrive at the most pressing question: How do we actually use the powerful new tools it has produced?

This article serves as a bridge from the theoretical to the practical. This is not a list of traditional prompt engineering hacks. The internet is filled with tips and tricks for coaxing a specific output from a language model for a single task. Instead, the advice that follows builds on our foundational understanding to offer something more durable: a general mindset for working with these tools. This approach is about engaging with language models in a way that allows you to get the best out of them without subcontracting your own critical thinking. It is a methodology for augmenting your intellect, not replacing it.

The principles you learn here are foundational, offering a universal toolkit for interacting with large language models in daily life—whether you are planning a vacation, trying to understand a complex news article, or drafting a simple email. In the following chapters (in the book), we explore how to adapt and intensify these practices for specialized, high-stakes professional environments. But first, every user must learn how to engage with these powerful yet fallible tools safely, critically, and effectively.

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A Methodology for Effective Interactions with Language Models

To move beyond simple queries and unlock the true potential of language models, we need a more structured approach. This methodology is divided into three parts: establishing the right Mindset, employing effective Tactics during the conversation, and building a System to make your successes repeatable.

The Mindset

The most significant shift is in your mental model. Instead of treating the model as a search engine, you should approach it as a conversational partner. This means recognizing that the interaction is iterative and that your most important role is to guide the conversation.

A key part of this mindset is adopting a Socratic (or inquiring) approach, where you use the model not just to get answers, but to help you ask better questions. This is invaluable for sensitive and important tasks.

For example, instead of starting with “Write an email asking for a raise,” a partner-based approach would be to ask the model to guide you: “I need to write an email to my manager to ask for a raise. What are the key pieces of information and evidence I should gather first to make the strongest possible case?” The model will then prompt you for your accomplishments and market data, helping you build your argument before a single word is written.

Similarly, when organizing a child’s birthday party, you could ask, “I’m planning a science-themed party for my 7-year-old. What are the key logistical details I need to consider to make sure it runs smoothly?” In both cases, you are using the model to help you define the problem, which is a far more powerful use of its capabilities.

The Tactics

With the right mindset, you can employ specific tactics to steer the conversation toward a high-quality outcome. The most fundamental tactic is to be explicit and strategic with your queries. To ground the model’s response in reliable information, tell it where to look.

A generic query for medical advice is risky, whereas a much safer prompt would be: “Search for information from the Mayo Clinic and the World Health Organization on the common symptoms of iron deficiency.” This specificity is also crucial when comparing complex options, like, for example, buying an EV, you can be as specific as: “Compare the Tesla Model 3, the Hyundai Ioniq 5, and the Ford Mustang Mach-E for a family of four. Focus on real-world range, charging speed on a standard home charger, and available cargo space.”

To get an even more robust answer, you can move beyond a single query and assemble a ‘committee of experts.’ A single language model will give you its most statistically likely answer, which might not be the most creative or well-rounded one. To overcome this, you can generate multiple, independent perspectives.

For the EV comparison, you could open three separate conversation windows. In the first, you’d ask the model to act as a pragmatic engineer and, perhaps, the model will argue for the Hyundai. In the second prompt, you’d ask it to be a tech enthusiast, and maybe it makes the case for the Tesla. In the third, you’d have it act as a family-focused reviewer, causing it to arguing for the Ford.

By copying these three independent analyses into a final chat window, you can then ask the model to act as a senior editor, synthesizing the competing viewpoints into a final, balanced recommendation that weighs factors like cost, range, and reliability.

Finally, after the model provides a response—either a single answer or a synthesized one from your committee—you can employ self-criticism as a final refinement tactic.

Once you have a draft of your email asking for a raise, you can prompt it: “Read the email you just drafted. Now, act as my manager who is busy and skeptical. What parts of this email are unconvincing? Is the tone too demanding or not confident enough?” This critical step often surfaces weaknesses that you might have missed, allowing you to create a much stronger final product.

The System

The final part of the methodology is to turn your successful interactions into a repeatable system. A common mistake is to treat prompts as disposable. A more powerful approach is to build a library of reusable prompts, thinking of them as personal “natural language programs.” The multi-step process you used to plan the birthday party can be saved as a “Kids’ Party Planner” template.

The Socratic prompt that helped you prepare for your salary negotiation can be generalized into a “Career Conversation Prep” tool. The ultimate expression of this principle is the use of features like OpenAI’s “Custom GPTs,” which allow you to encapsulate a complex task into a dedicated tool that you or your team can use with a simple request.

A Practical Example

To see how these principles combine into a powerful workflow, let’s walk through a comprehensive, real-world task: planning a 10-day family vacation to Italy.

Rather than beginning with a vague request like “plan a trip,” the process starts by applying the Socratic approach. You would first ask the model to frame the problem for you: “I want to plan a 10-day family vacation to Italy. What key information do you need from me to create the best possible itinerary?”

This immediately shifts the dynamic, positioning the model as a guided partner. In response, it would act as a consultant, asking for crucial details like the number of travelers, the children’s ages, your budget, family interests, and preferred travel pace.

Once you’ve provided this context, the next step is to ensure alignment. You would instruct the model to synthesize and confirm the constraints: “Great, thank you. Based on my answers, please summarize all of my constraints for this trip in a structured list.”

With a clear, confirmed set of requirements, you can then confidently ask for a first draft. The iterative heart of the process begins now. Upon receiving the initial itinerary, you would employ the self-criticism tactic: “This is a good start. Now, act as a skeptical travel agent. Criticize this itinerary and tell me what’s missing or what could go wrong.”

The model might point out that visiting three major cities in ten days is too ambitious for a family with young children. Based on this valuable feedback, you can guide the revision, continuing this loop of drafting and critiquing until the plan is refined to your satisfaction. Only then would you ask for the final, detailed output.

The final, powerful step is to generalize this success. You would ask the model to convert the entire conversation into a reusable “Family Vacation Planner” template, complete with placeholders for key details. This turns a one-time effort into a valuable, programmable asset for future trips, demonstrating the true power of thinking of prompts as reusable programs.

Common Pitfalls for the Everyday User

The good practices above are designed to improve the quality of a language model’s output. This section focuses on the mental traps and risks you must be aware of to use these tools safely.

The “Eliza Effect” and Misplaced Trust

Because chatbots are designed to be conversational and helpful, it’s easy to start treating them as if they have genuine understanding, intentions, or even consciousness. This is a modern version of the “ELIZA effect” we discussed in the history chapter. The danger is that this leads to misplaced trust, where we stop critically questioning the model’s output because it feels so confident and knowledgeable. This is the psychological trap that makes us vulnerable to hallucinations; we are less likely to fact-check a “partner” than a machine.

Cognitive Offloading and The “Lazy Brain” Problem

The ease of asking a language model to summarize an article, draft an email, or brainstorm ideas can lead to a subtle but significant danger: cognitive offloading. By outsourcing the fundamental work of thinking, synthesizing, and structuring our thoughts, we risk letting our own critical thinking and creative muscles atrophy. The goal is to use these tools to think better, not to think less. Over-reliance can make us less capable problem-solvers in the long run.

The Privacy Risk of Casual Conversation

In a casual conversation with a chatbot, it’s easy to forget that you are interacting with a complex system run by a corporation. Users often paste sensitive personal information—medical details, financial data, private emails, proprietary work content—into public language models without considering where that data goes, how it’s used for future training, or who might have access to it. What you tell the model does not stay between you and the model.

You Are the Final Authority

The techniques above teach you how to get better raw material from the language model. This final principle is about what you, the human, must do with that material. It is the most critical step in using these tools responsibly.

First, never trust, always verify. The language model is an unreliable narrator. Treat its output as a well-written first draft, not a finished fact. For any critical piece of information—a date, a statistic, a medical suggestion, a legal point—you must verify it using an independent, authoritative source. The model can help you find potential sources, but you are the fact-checker.

Second, synthesize, don’t just copy-paste. The model’s output is information; your goal is knowledge. The most important work happens after the model has responded. Your job is to synthesize its suggestions with your own experience, judgment, and goals. The model can generate a list of tourist sites for your Italy trip, but only you can synthesize that into a vacation plan that feels right for your family.

Finally, own the outcome. The language model is a tool, and you are the user. Any decision made, any email sent, or any action taken based on the model’s output is your responsibility. This principle of accountability is non-negotiable. The model is an assistant that can help you think, but it is not a replacement for your personal judgment.

Conclusion

The journey from a novice user to a skilled one is not about memorizing clever prompts; it’s about a fundamental shift in mindset. Instead of treating generative AI as a vending machine for answers—an approach fraught with risks of shallowness, bias, and error—we’ve seen the power of engaging it as a conversational partner.

The practices outlined in this article—the Socratic method, strategic querying, and, most importantly, critical verification—form a framework for responsible engagement. This framework places you, the user, firmly in the driver’s seat.

The quality of the model’s output is not a feature of the model alone; it is a direct reflection of the quality of your guidance and the rigor of your review. You are not just a prompter; you are a director, a critic, and a synthesizer. This is what makes these powerful tools ‘mostly harmless’: not their inherent nature, but our commitment to using them with critical awareness and human authority.

By mastering these foundational skills, you are not just learning to use a new tool. You are developing a new form of literacy for the 21st century. As we move into the specialized applications for knowledge workers, developers, and creatives in the following chapters (of the book), this ability to think with AI, not just ask of it, will be your most valuable asset.

Thanks again for reading. If you want to dive deeper into Artificial Intelligence and learn to make the best out of it, from a techno-pragmatist, human-centered, responsible perspective, please check out my book Mostly Harmless AI.