If you only have 2 hrs, this is how to become an AI PM Layoffs and the Rise of AIPMs: While many companies are laying off PM teams, AIPM (AI Product Manager) roles are surprisingly secure. The demand for AIPMs is growing, and the role is less about replacing traditional PMs and more about augmenting their skills with AI. Can Anyone Become an AIPM?: Yes, almost every PM can become an AIPM, either by using AI tools in their workflow (AI-powered PM) or by building AI into their products (AI product PM). It's not an either/or situation but rather an "X" – a PM can be a "fintech X AIPM" or "healthcare X AIPM," etc. The core skills of product management remain relevant, but AI enhances them. Five Key AIPM Skills: The video outlines five essential skills: AI Prototyping: Using tools like Cursor (a VS Code fork) or others (Lovable, Bolt, Replit, Vercel, V0) to quickly build AI-powered prototypes. Cursor is recommended for its flexibility and control, although it has a steeper learning curve. The video demonstrates building an agent-based trip planner using Cursor and Claude 4. This involves iterative prompting, allowing the agent to generate code, create directories, and even handle errors on the fly. The process highlights the importance of iteration and learning to ask effective questions of the AI. The video also demonstrates handling errors and interruptions in the process. Observability: Understanding how your AI application works through tracing. The video uses Arise, an observability tool, to visualize the interactions within an agent-based system. This allows for a deeper understanding of the agent's actions and decision-making process. Implementing tracing involves adding a line of code to wrap functions and track units of work. Evaluation (Eval): Quantifying the performance of your AI application. The video demonstrates using Arise to create and run A/B tests on different prompts and models. Three types of evaluation are discussed: human labeling, code-based evaluation, and using LLMs as judges to provide automated feedback. The importance of aligning LLM judge evaluations with human labels is emphasized. Rag, Fine-tuning, and Prompt Engineering: Understanding the differences and applications of these techniques. A diagram illustrates their distinctions: prompt engineering adjusts tone and instructions; RAG provides context from large datasets; fine-tuning specializes the model itself. The video shows how adjusting prompts can significantly impact the output and efficiency of the system. Working with AI Engineers and Researchers: Collaborating effectively with AI engineers and researchers on longer-term projects. This involves understanding the technical details of AI systems, providing valuable feedback based on user experience, and communicating effectively using shared tools and data. The video suggests that AIPMs should be involved in defining evaluation metrics and requirements. Common Mistakes to Avoid: Lack of Side Projects: Not having side projects to practice using AI tools and build a portfolio. Waiting for Perfect Models: Delaying learning and building until models are perfect. Over-Automating: Trying to automate too much of the PM's job too early. Instead, use AI to augment analysis, research, and idea generation. Two-Hour-a-Week Plan to Become an AIPM: Try the tools: Experiment with AI tools for personal projects. Build AI intuition: Understand how AI systems work by analyzing existing products and code. Apply your learning: Build your own AI-powered side projects to continuously learn and improve. The AIPM Job Market: While job titles might not always explicitly say "AIPM," many PM roles now incorporate AI, offering higher salaries. The demand for AIPMs is growing faster than the supply, making it a future-proof career path. A LinkedIn search for "AI Product Manager" shows numerous job postings. Aman Khan's Resources: Aman Khan, the guest speaker, can be found at ammank.ai, LinkedIn, and Twitter. He also offers a Maven course on AI prototyping for PMs. If you only have 2 hrs, this is how to become an AI PM AI Product Management (AIPM): A Crash Course Study Guide This study guide summarizes key concepts from a podcast episode on AI Product Management. Can Anyone Become an AIPM? The role of a Product Manager ( PM) is evolving due to increasing AI integration. An AIPM can be defined as a PM who either adopts AI in their daily workflow or builds AI into their product . It's not an "either/or" situation; rather, it's an extension of existing PM roles . Example: A fintech PM can also be an AIPM by using AI to improve their workflows. Almost every PM will incorporate AI into their work, either by using AI tools or building AI features . Five Key AIPM Skills This section outlines five crucial skills for successful AIPM. 1. AI Prototyping Cursor: A VS Code fork, recommended for its control and flexibility in prototyping. Other tools like Lovable, Bolt, Replit, Vercel, and V0 are also useful, each with its strengths. Agent-based systems: Prototyping with agents allows for more complex interactions and control over the system. Example: Building a trip planner agent using LangChain. Iterative process : Focus on iterative development and refining prompts to achieve desired results. Multimodal agents: Agents that can process and use various data types (text, images, etc.). 2. Observability Tracing: A standard method for monitoring the calls your server makes. Visualizes the flow of data and interactions within an agent-based system. Arise: An example of a company providing tools for observability and tracing. Implementation: Involves installing a tracing package and adding decorators to your code to track units of work. 3. Evaluation (Eval) Quantifying system performance: Moving beyond "vibe coding" to a more data-driven approach. Data sets: Creating data sets to test and evaluate changes to your system. Three types of eval: Human labeling: Manually labeling data as good or bad. Code-based eval: Using code to check specific aspects of the output. LLM as judge: Using an LLM to evaluate the quality of the agent's output. Iterative improvement: Using eval results to improve prompts, agents, and the overall system. Example: Using human labels to identify discrepancies between LLM judgment and desired outcomes. 4. Rag, Fine-tuning, and Prompt Engineering Prompt engineering: Adjusting the tone and instructions in prompts to improve output quality. Low effort, high impact. Rag (Retrieval Augmented Generation): Providing context from a large dataset to enhance the agent's understanding and response . Medium effort, high impact. Fine-tuning: Adjusting the model's parameters to specialize it for a specific task. High effort, high impact. Trade-off between specialization and generalization. 5. Working with AI Engineers and Researchers Collaboration: AIPMs need to understand the technical aspects of AI development to effectively collaborate with engineers and researchers. Data-driven feedback: Provide data-driven feedback based on eval results to guide the development process. Shared tools and platforms: Working within the same tools and platforms as the engineering team facilitates communication and collaboration. Common Mistakes to Avoid Lack of side projects: Building side projects helps develop practical skills and demonstrate initiative. Waiting for perfect models: Start building now, even with imperfect models, to gain experience and stay ahead of the curve. Automating too much: Use AI to augment, not replace, your work. Focus on leveraging AI for analysis and research. Two Hours a Week to Become an AIPM Try the tools: Experiment with different AI tools and understand their capabilities and limitations. Build AI intuition: Develop an understanding of how AI systems work by analyzing existing products and code. Apply your learning: Build a side project to apply your knowledge and stay motivated to learn new technologies. Key Takeaways The role of the PM is evolving rapidly due to the integration of AI. AIPMs need a diverse skillset encompassing prototyping, observability, evaluation, prompt engineering, and collaboration with AI engineers. Continuous learning and iterative development are crucial for success in this field. There is a growing demand for AIPMs, and those who acquire the necessary skills will be well-positioned for career advancement.