AI for Product Managers | ProductMe

Artificial Intelligence for Product Managers: A Practical Guide

Is there such a thing as an “AI Product Manager”?

Let’s clear up a common misconception: There is no such thing as an “AI Product Manager.” While the term is popular and generates a lot of discussion online, it is misleading. Product managers (PM) focus on the overall success of the product, while artificial intelligence or machine learning (ML) engineers are responsible for building and improving AI models.

Most product managers don’t develop AI models themselves. Instead, they will use existing ones, e.g. OpenAI’s GPT, Meta’s Camel, Google Geminior human claude. The heavy lifting of creating and maintaining these models is done by the engineering team.

If anything, the term “artificial intelligence product manager” may be more appropriate for future tools, such as virtual product manager assistants, that can help identify customer needs, align them with business goals, and break them down into tasks. While the idea is exciting, it’s still a bit futuristic and far from replacing today’s human PMs.

In the era of artificial intelligence, what is constant for product managers?

Artificial intelligence does not change the fundamentals of product management. (I’ve shared my thoughts on how AI will change the job of product managers here. It was already a year ago, but so far it’s pretty mature.

1. Understand user issues: Understand what problem the user wants to solve.
2. Create a solution: Work with your team to come up with solutions to these problems.
3. Well-defined tasks: Break down the solution into steps that the team can execute.

What’s new is the need to understand artificial intelligence concepts so that you can make better decisions and communicate effectively with your team.

Basic LLM terminology for PM

You don’t need to be a technical expert to use artificial intelligence, but it helps to know some key terms:

• Token: Artificial intelligence models break text into smaller pieces, called tokens. Markers can be words, part of words, or punctuation marks.
• context window: This is the amount of information (measured in tokens) that the AI ​​can process at one time, including your input and the AI’s response.
• System prompt: Instructions that tell the AI ​​how to behave or respond.
• User tips: The text you enter to interact with the AI.
• temperature: Controls the creativity or randomness of AI responses. Cold temperatures can provide simple answers, while higher temperatures can provide more variety.

Other settings such as frequency penalty or There is punishmentto fine-tune the behavior of the AI, but you probably won’t need them in most cases.

How an LLM works at a high level

Large Language Models (LLMs) predict what comes next in a sentence based on the text it has seen so far. They are trained on large amounts of material to model meaningful conversations, summarize information, or generate content.

It’s important for case managers to know that LL.M.s are good at tasks like text generation and summarization, but may struggle with complex reasoning or up-to-date real-world knowledge.

Tips for learning to be a PM

Writing a good prompt is like good communication—clear, concise, and to the point. As a product manager, you often need to write prompts to test ideas or generate content quickly.

Tips for writing prompts:

1. Be specific: Clearly explain what you want the AI ​​to do, including format or tone.
2. Add context: Provide enough context to guide the AI.
3. Refine and iterate: Adjust tips based on the AI’s output for better results.

Prompting is a skill worth practicing because it is becoming a common way to interact with artificial intelligence tools.
If your product has AI-based features, in my experience you as a product manager will be involved in building prompts in one form or another. Most likely, you will be considered one of the best (if not the best) communicator on the team. Therefore, you would be the obvious choice to provide initial instructions to the AI, and may also be involved in the fine-tuning process.

Practical tips for PMs

1. experiment: Get hands-on experience using tools like ChatGPT, Claude or Bard.
2. Work with engineers: Work closely with the technical team to connect product goals with technical possibilities.
3. keep updated:Follow the AI ​​trend because things are changing fast. A good source to stay up to date is Hacker News – The most relevant changes will usually appear on the homepage.
4. focus on value: Find areas where AI can improve your product, such as saving time, enhancing user experience, or driving new revenue. Don’t make the mistake of adding artificial intelligence for the sake of adding artificial intelligence.

in conclusion

Artificial intelligence doesn’t change the core of product management – ​​it’s still about solving user problems. But it does give you powerful new tools. By understanding what artificial intelligence can and cannot do, you can make smarter decisions and help your team succeed.

Keep learning, stay curious, and remember: AI is here to help you, not replace you. At least not yet 🙂