Presented at AI Engineer. Finance used to be bottlenecked on producing content. It is now bottlenecked on verifying it. An analyst who once spent the day building a model now spends it checking whether a machine’s output can be defended to a client, a committee, or a regulator. ...
Presented at AI Engineer. Most companies hire forward deployed engineers as an extension of go-to-market: a technical body to keep a large account happy. That’s the wrong frame. FDE is a product strategy. The job is to find the real problem, ship the smallest thing that solves it, and turn what you learned into leverage for the product. ...
Most people now use ChatGPT, Claude, or Gemini every week without a clear mental model of what’s happening underneath. That’s fine right up until the model is confidently wrong and you have no way to tell. AI Model Basics is a new 20-minute LinkedIn Learning course aimed at exactly that gap. What the Course Covers How large language models actually work — enough mechanism to reason about them, without the math What prompting does and why it changes the output as much as it does Why models produce wrong answers, and what kinds of wrong to expect Personalization — what these tools can and can’t retain about you How to evaluate and improve what you get back instead of accepting the first response Who This Is For Anyone using AI tools at work who wants to understand them rather than just operate them. It’s beginner-level and assumes no technical background — no coding, no prior AI experience. ...
Most AI projects don’t fail at the model. They fail somewhere between the demo that impressed everyone and the system that has to survive contact with a real customer’s data, workflows, and definitions. I published a new LinkedIn Learning course on the role that closes that gap: Forward Deployed Engineering in the Age of AI. Why This Course Exists AI deployment differs fundamentally from a traditional SaaS rollout. A SaaS product is largely the same on day one for every customer. An AI system is not — its behavior depends on the customer’s data quality, their vocabulary, their edge cases, and their tolerance for being wrong. That means the work of making it succeed happens in the field, not at headquarters. ...
Part of Lenny Rachitsky’s “The AI-Native Product Manager” free workshop series on Maven. Most products fail not because of bad engineering, but because teams build the wrong thing. They either overbuild features nobody asked for or underbuild solutions that miss the real problem. Forward Deployed Engineering (FDE) is the antidote. What is FDE? Forward Deployed Engineering is a methodology where technical teams embed directly with customers to witness how software meets reality. Instead of building from assumptions, FDEs develop customer instinct by being in the field — watching real users interact with real products under real conditions. ...