You might question why you’d build your own custom GPT on AWS when OpenAI now offers the same feature. The answer is relatively straightforward:
You can’t solely be a wrapper for OpenAI. If you only develop a custom GPT with OpenAI’s platform and some cookie cutter content, it’ll typically perform well when everyone is asking similar queries. For instance, a diet coach would probably work pretty well. There’s only a finite number of known strategies to cut calories and increase caloric expense. But in some other areas, like marketing, answers are too context dependent for generic advice. What works in b2b marketing doesn’t work in marketing a product for teens, and what works in marketing for teens doesn’t work for boomers, and what works for boomers doesn’t work in marketing supplements, etc. You have the chance to capture more value by learning how to inject that knowledge into an LLM. You become the LLM’s context-specific meta-intelligence.
Of course, before we can do that, we need that DB from step 0, where we store the embeddings with the original text, indexed for fast similarity search.