Harnessing Large Language Models

4 min read

First published February 13, 2025, and edited for style.

We live in a remarkable era, a time when technology not only makes our lives easier but also helps us grow personally and professionally. Among the tools now available, one stands out for its potential to support professional development: large language models such as ChatGPT, developed by OpenAI. These models can offer substantial benefits to people across diverse professional fields. They can help us be more effective in our work, advance in our careers and make a greater impact in our chosen fields.

Consider for a moment the vast repository of human knowledge. We often spend a considerable amount of time searching for information, studying and trying to remember facts or concepts. With tools like ChatGPT, we can have instant access to a broad array of information and insights. These models can quickly answer questions, provide explanations and even generate ideas or hypotheses, drawing on their training across a diverse range of sources.

For professionals eager to expand their skills and knowledge, this is a significant opportunity. You can use ChatGPT to explore new areas of interest, to deepen your understanding of your field or to prepare for presentations and discussions. You can also use it to sharpen your problem-solving, to brainstorm creative solutions or to refine your strategic thinking.

However, it's important to remember that ChatGPT, like any tool, is most effective when used appropriately. Just as a carpenter must learn how to use a hammer or a saw, we must learn how to use AI models. This involves understanding the model's capabilities as well as its limitations, and developing ways to ask effective questions and interpret the responses.

For those interested in using ChatGPT for professional development, we suggest the following steps

Explore and Understand: Spend some time exploring what ChatGPT can do. Ask it questions, try out different types of prompts and see how it responds. Understand its limitations. At the time of writing, for example, it had been trained on a vast range of data but had no access to real-time or proprietary information.
Be Specific and Thoughtful: When asking ChatGPT for help or insights, be as specific as you can. If you're vague, the model might not provide the precise information you're looking for.
Experiment and Learn: Don't be afraid to experiment and learn from the process. Try using ChatGPT in different professional situations, such as preparing for meetings, brainstorming ideas or exploring new topics.
Reflect and Refine: After using ChatGPT, take some time to reflect. Did the model provide useful information? How can you refine your prompts or questions to get better results next time?

The arrival of large language models like ChatGPT marks a significant step forward in AI. It is a tool we can use to our advantage, and as we continue to experiment, learn and grow, it opens new possibilities for professional development.

As Clayton Christensen once said, "It's easier to hold your principles 100 percent of the time than it is to hold them 98 percent of the time." In that light, let's apply our principles of learning, growth and curiosity all of the time, making full use of tools like ChatGPT and always striving to be better informed and more creative.

The future, after all, isn't something that happens to us. It's something we create. Let's use AI to help create a better informed and more successful future for ourselves.

What has changed since this was written

This piece was first published in early 2025. It has been edited for style only, because the reasoning holds and the reasoning is the point. The capability assumptions are the part that has moved.

Read it for its advice on how to work with these tools rather than as a description of what they can currently do. Two things are worth checking against the present rather than taking from the text.

Assume the capability floor is higher than described

The piece notes that these tools had no access to real-time or proprietary information. Many now search the web or connect to an organization's own documents and systems. That widens what they can do, and it makes data governance part of using them.
Treat any limitation the piece mentions as something to re-test. Planning around a limitation that has since gone is as costly as ignoring one that remains.

The advice to be specific and to reflect has not dated

Being specific means supplying your goals, your context and your standards. The tool cannot supply those for you, however capable it becomes, so better capability raises the value of clear framing.
The habit of reflecting on output before using it matters more as quality improves, because confident output becomes easier to accept without checking.

Harnessing Large Language Models

4 min read

First published February 13, 2025, and edited for style.

For those interested in using ChatGPT for professional development, we suggest the following steps

Explore and Understand: Spend some time exploring what ChatGPT can do. Ask it questions, try out different types of prompts and see how it responds. Understand its limitations. At the time of writing, for example, it had been trained on a vast range of data but had no access to real-time or proprietary information.

Be Specific and Thoughtful: When asking ChatGPT for help or insights, be as specific as you can. If you're vague, the model might not provide the precise information you're looking for.

Experiment and Learn: Don't be afraid to experiment and learn from the process. Try using ChatGPT in different professional situations, such as preparing for meetings, brainstorming ideas or exploring new topics.

Reflect and Refine: After using ChatGPT, take some time to reflect. Did the model provide useful information? How can you refine your prompts or questions to get better results next time?

The future, after all, isn't something that happens to us. It's something we create. Let's use AI to help create a better informed and more successful future for ourselves.

What has changed since this was written

This piece was first published in early 2025. It has been edited for style only, because the reasoning holds and the reasoning is the point. The capability assumptions are the part that has moved.

Read it for its advice on how to work with these tools rather than as a description of what they can currently do. Two things are worth checking against the present rather than taking from the text.

Assume the capability floor is higher than described

The piece notes that these tools had no access to real-time or proprietary information. Many now search the web or connect to an organization's own documents and systems. That widens what they can do, and it makes data governance part of using them.

Treat any limitation the piece mentions as something to re-test. Planning around a limitation that has since gone is as costly as ignoring one that remains.

The advice to be specific and to reflect has not dated

Being specific means supplying your goals, your context and your standards. The tool cannot supply those for you, however capable it becomes, so better capability raises the value of clear framing.

The habit of reflecting on output before using it matters more as quality improves, because confident output becomes easier to accept without checking.