Great post! The question of complexity is really key. I’ve noticed that many PMs are pushed toward implementing complex AI solutions, even when a simpler approach could work. I’m not just thinking about final products, but also early-stage developments or even PoCs, which can benefit greatly from lower-complexity solutions. Context, engineering knowledge, and AI literacy also make a big difference! I’ve had conversations with stakeholders advocating for more complex approaches that required careful convincing.
I hear you. That is one of the main reasons I always say capability trumps category: if we can achieve the same results with less/er technology/complexity, we should 100% choose that path.
Brilliant breakdown on the complexity ladder! The framing around "how much autonomy does this problem need" is exactly the right question. I've seen too many teams jump straight to agentic solutions when a well-tuned RAG system woudl have been faster and more reliable. The Context Brief idea is solid too, kinda like having a mini-spec for what the model actually "sees" at runtime.
Great simplification on context engineering. I second the disagreement and adding it to your class. I came from AI Platform product mindset and in the options given I would see one as platform another as a use case leveraging the platform context.
Great framing, Shaili! Context engineering absolutely matters for PMs.
If you think about it, many PM tasks are already about connecting the dots, and knowing what to exclude. Prioritization, scoping, stakeholder comms: it’s all context engineering, sculpting and resculpting.
Great post! The question of complexity is really key. I’ve noticed that many PMs are pushed toward implementing complex AI solutions, even when a simpler approach could work. I’m not just thinking about final products, but also early-stage developments or even PoCs, which can benefit greatly from lower-complexity solutions. Context, engineering knowledge, and AI literacy also make a big difference! I’ve had conversations with stakeholders advocating for more complex approaches that required careful convincing.
I hear you. That is one of the main reasons I always say capability trumps category: if we can achieve the same results with less/er technology/complexity, we should 100% choose that path.
Brilliant breakdown on the complexity ladder! The framing around "how much autonomy does this problem need" is exactly the right question. I've seen too many teams jump straight to agentic solutions when a well-tuned RAG system woudl have been faster and more reliable. The Context Brief idea is solid too, kinda like having a mini-spec for what the model actually "sees" at runtime.
Exactly and thank you!
Great simplification on context engineering. I second the disagreement and adding it to your class. I came from AI Platform product mindset and in the options given I would see one as platform another as a use case leveraging the platform context.
What a great thought, I will have to keep platform product management in mind too!
Great framing, Shaili! Context engineering absolutely matters for PMs.
If you think about it, many PM tasks are already about connecting the dots, and knowing what to exclude. Prioritization, scoping, stakeholder comms: it’s all context engineering, sculpting and resculpting.
I love that! Yes - we, PMs, are already contexting engineering even before context engineering was a "thing" (technical term)!