One of the biggest missed opportunities in liquid handling automation is that assistance tools tend to assume a single user profile. Whether it’s an OEM’s built-in wizard or a generic AI prompt, the system treats a first-time programmer and a ten-year veteran the same way. Real assistance has to begin by understanding where the user is starting from — not just what they want to build, but what they already know, what they already have, and what kind of help would actually move them forward. A novice automating their first plate-based assay and an experienced developer scaling a vendor kit protocol are solving fundamentally different problems, and the guidance they receive should reflect that from the first interaction. I’m working on a framework of “Guidance Classes” that tailor the entire development workflow — from intake through testing and validation — based on the user’s actual starting point. Someone building from scratch gets walked through method design documentation and learns why each liquid handling primitive matters. Someone importing an existing method gets help understanding what they already have before planning changes. An expert gets a co-pilot that stays out of the way. The system should feel invisible when it’s working well — sound, actionable guidance at every step without unnecessary hand-holding or gatekeeping.
The other critical piece is hardware agnosticism. Vendor software layers impose their own conceptual models on method development, and those models carry baggage that shapes how people think about their science. But the method and its outcome are what matter — not a manufacturer’s opinion about how you should structure your workflow to fit their UI paradigm. By building on something like PyLabRobot, the AI guidance layer can reason about liquid handling operations cleanly: aspirate, dispense, mix, transfer. The user thinks in terms of their protocol and their science. The system handles translation to whatever hardware is on the bench. This also means that proven approaches become portable — a well-characterized plate reformatting routine shouldn’t have to be reinvented every time someone changes platforms.
I’d love feedback from the community on this direction, particularly from people at different experience levels. What does useful AI assistance actually look like for how you work? Where do current tools fall short, and where do they get in the way?