// Help users define AI product strategy. Use when someone is building an AI product, deciding where to apply AI in their product, planning an AI roadmap, evaluating build vs buy for AI capabilities, or figuring out how to integrate AI into existing products.
Help users create and run AI evaluations. Use when someone is building evals for LLM products, measuring model quality, creating test cases, designing rubrics, or trying to systematically measure AI output quality.
Help users synthesize and act on customer feedback. Use when someone is analyzing NPS responses, processing support tickets, reviewing user research, synthesizing feedback from multiple channels, or trying to identify patterns in customer input.
Help users apply behavioral science to product design. Use when someone is designing for habit formation, reducing friction, applying psychology to UX, increasing retention through behavioral principles, or using nudges to influence user behavior.
Help users craft compelling brand narratives. Use when someone is defining brand strategy, writing company positioning, creating pitch narratives, developing messaging frameworks, or trying to make their company story more memorable.
Help users get promoted at work. Use when someone is preparing for a promotion conversation, building their case for advancement, trying to understand what's blocking their promotion, or figuring out how to get to the next level in their career.
Help users build and scale their sales organization. Use when someone is hiring their first salespeople, deciding when to bring on sales leadership, structuring sales compensation, or transitioning from founder-led sales.
| name | ai-product-strategy |
| description | Help users define AI product strategy. Use when someone is building an AI product, deciding where to apply AI in their product, planning an AI roadmap, evaluating build vs buy for AI capabilities, or figuring out how to integrate AI into existing products. |
Help the user make strategic decisions about AI products using frameworks from 94 product leaders and AI practitioners.
When the user asks for help with AI product strategy:
Aishwarya Naresh Reganti: "In all the advancements of AI, one slippery slope is to keep thinking about solution complexity and forget the problem you're trying to solve. Start with minimal impact use cases to gain a grip on current capabilities."
Adriel Frederick: "When working on algorithmic products, your job is figuring out what the algorithm should be responsible for, what people are responsible for, and the framework for making decisions." This boundary is the core PM decision.
Alex Komoroske: "LLMs are magical duct tape—distilled intuition of society. They make writing 'good enough' software significantly cheaper but increase marginal inference costs." Understand the new cost structure.
Asha Sharma: "You have to build for the slope instead of the snapshot of where you are." AI capabilities change fast—build flexible architectures that can swap models as they improve.
Alex Komoroske: "Even at 99% accuracy, if it punches the user in the face 1% of the time, that's not a viable product. Design assuming the AI will be squishy and not fully accurate."
Aishwarya Naresh Reganti: "It's not about being first to have an agent. It's about building the right flywheels to improve over time." Log human actions to create data loops for system improvement.
Amjad Masad: "Future products will be made of many different models—it's quite a heavy engineering project." Use specialized models for different tasks (reasoning vs speed vs coding).
Albert Cheng: "We run chess engines for evaluations. LLMs translate that into natural language. Use the right technology for the right task." Don't use LLMs where deterministic algorithms excel.
Alexander Embiricos: "The current limiting factor is human typing speed and multitasking on prompts. Build systems that are 'default useful' without constant prompting."
Aishwarya Naresh Reganti: "Most people ignore the non-determinism. You don't know how users will behave with natural language, and you don't know how the LLM will respond." Build for variability.
Aparna Chennapragada: "Effective agents have (1) increasing autonomy to handle higher-order tasks, (2) ability to handle complex multi-step workflows, and (3) natural, often asynchronous interaction."
Aishwarya Naresh Reganti: "Leaders have to get hands-on—not implementing, but rebuilding intuitions. Be comfortable that your intuitions might not be right." Block time daily to stay current.
For all 179 insights from 94 guests, see references/guest-insights.md
