Quality AI
Generative AI is inching its way into tons of consumer products.
Gmail will help you draft an email.
Linkedin will write a post for you.
Github Copilot will generate code for you.
Notion AI drafts ideas for you or continue writing where you left off.
Very few spaces only will be without AI infusion over the next couple of years. Everybody will claim to have generative AI, but the quality will vary dramatically between these services. For the time being, most Generative AI is just a starting point, a draft, an idea generator. Only in rare cases will it produce content you can send out the door without review. The outputs can be repetitive, generic, and often straight-up wrong.
Infusing Generative AI into areas of your business can create significant value (more on business use cases in a future email).
What shapes the quality of a Large Language Model output? Three key factors:
The structure and quality of the prompt provided to the model.
The model parameters (the amount of randomness or uniqueness allowed, max response length, pattern frequency and repetitive content allowed, etc.)
The data the model is trained on.
Number #1 is likely a skill business and analytical users will learn.
Number #2 is a deep technical specialization from an AI engineer.
Number #3 will fall onto the data team.
Improving the quality of the AI output will require iterative collaboration between these groups
Business teams and data teams
Building better data experiences
With LLMs.
I’m here,
Sawyer
