Intro
Understanding how these things work matters.
Not because you’re going to build the next GPT yourself, but because understanding just enough of how LLMs and vector math work can change how you think about products, teams, and strategy. It can help inspire better solutions, make smarter tradeoffs when AI promises start sounding magical, and maybe even help you call BS when needed. Whatever strategic product decisions you may be making, your implementation team could be internal or perhaps a contract shop. In either case, there’s operations impact and costs that will likely impact your roadmap. If you also have P&L responsibility, you’re going to need to look at the costs here with regard to your business case. And if you don’t, chances are you may be the one who still has to justify the spend to others. As usual in product manager land, even if you’re not the one executing the actual work, you likely need to understand enough about the pieces to know what they can do and what this might cost.
tl;dr
- LLMs turn text into numbers using math called vector embeddings. We’re going to look at this below.
- These vectors live in a high-dimensional space, where “distance” equals “semantic similarity.” Again, we’ll look at an example below.
- Transformers (not the ones from the comics/movies) are the model architectures that makes GPT-style LLMs so powerful.
- All this lets us build apps that “understand” language enough to generate answers, categorize, summarize, translate, and more.
- But it’s still math, not magic. And it’s expensive from a lot of perspectives. The question is where do we want to take the expense hit(s) and for what level of benefit.
