The "Schillace Laws" were formulated after working with a variety of Large Language Model (LLM) AI systems to date. Knowing them will accele

Schillace Laws of Semantic AI

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2023-03-21 22:30:07

The "Schillace Laws" were formulated after working with a variety of Large Language Model (LLM) AI systems to date. Knowing them will accelerate your journey into this exciting space of reimagining the future of software engineering. Welcome!

Don’t write code if the model can do it; the model will get better, but the code won't. The overall goal of the system is to build very high leverage programs using the LLM's capacity to plan and understand intent. It's very easy to slide back into a more imperative mode of thinking and write code for aspects of a program. Resist this temptation – to the degree that you can get the model to do something reliably now, it will be that much better and more robust as the model develops.

Trade leverage for precision; use interaction to mitigate. Related to the above, the right mindset when coding with an LLM is not "let's see what we can get the dancing bear to do," it's to get as much leverage from the system as possible. For example, it's possible to build very general patterns, like "build a report from a database" or "teach a year of a subject" that can be parameterized with plain text prompts to produce enormously valuable and differentiated results easily.

Code is for syntax and process; models are for semantics and intent. There are lots of different ways to say this, but fundamentally, the models are stronger when they are being asked to reason about meaning and goals, and weaker when they are being asked to perform specific calculations and processes. For example, it's easy for advanced models to write code to solve a sudoku generally, but hard for them to solve a sudoku themselves. Each kind of code has different strengths and it's important to use the right kind of code for the right kind of problem. The boundaries between syntax and semantics are the hard parts of these programs.

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