User's framework for minimizing drift and genericity in open-ended AI response.

Pretty much anyone using an AI chatbot has gone through the frustration of a response that isn't exactly what they wanted. Problems include hallucinated, drifted, generic, and inconsistent output. Sometimes all of these happen; other times, one or more.

People try prompt improvisation, prompt tweaking, prompt packs, prompt libraries, trial and error, tries and retries. Sometimes it works; other times, it does not.

Too many details in a prompt cause prompt bloating and do not solve the problem. Adding context helps, but too much context creates problems.

AI agents and skills do not solve this problem with open-ended work either.

People give up, thinking LLMs are probabilistic and cannot work better.

That is not true.

It may never be as deterministic as systems in terms of consistency and reliability, but it can be mostly consistent and reliable.

Similarly, users can create a path with an LLM using the right framework. Users can guide their request through the LLM toward their desired outcome by using the right framework.

Learn to use this science-backed, proven framework.

It is based on research (https://papers.ssrn.com/sol3/papers.cfm?abstract_id=6516618) and has been tested in practice.