“That’s not what I meant!”
What if the original meaning stayed with an idea as it moved between people, models, documents, and workflows?
“That’s not what I meant!”
How often is that sentiment is expressed in organizations every day? How much wasted time, energy, and AI token burn is spent on ensuring that we have the right context? And, even with all of the flaws in our human-to-human communications, you would think that the other-worldly smarts of AI would get it right. But, think about how many times you have to keep a prompt chain going longer than it should or reset your AI’s context because it goes off in a direction that is way off base. Think about this exponentially occurring through an organization. That results in token burn of massive proportions.
In any organization, someone comes up with an idea. They share it with others, gain consensus, maybe even write a business case. Somewhere along the way, others agree and it transitions from an idea to actual work. Maybe it becomes an RFP and vendors bid on it to make it a reality. The idea travels from a concept to documents to processes to workflows. It can travel to vendors who have their own documents, processes and workflows. There can be numerous AI models in the background during all of these travels. Once the work is done, we often congratulate ourselves for completion, but the outcome may not mirror the original idea.
What if the original semantics stayed with the idea the entire time of travel from person to person, LLM to LLM, workflow to workflow in all of these different contexts?!
What is the value if we fixed this problem?
We’d optimize content and reduce data chaos.
We’d drastically reduce AI costs by lowering token burn.
We’d increase deployment velocity and eliminate semantic drift.
Best of all, we’d eliminate the headaches of contextual semantic misunderstandings with our AI friends.
Interested to see this in action? Leave a comment and we’ll show you how it works!
#ai #idea #contextengine #semantics