Why I asked. I had assumed a classifier's main job was filtering information before a larger model saw it. I wanted to understand what changed when software owned the workflow.
Was the main benefit of a decision model classifying information before sending it to a larger language model?
I learned that filtering was one use. A narrow judgment could also be the last AI step: code could use the result directly. The proposed pattern was shared context, several bounded questions, then explicit application rules. The underlying judgments still needed evaluation.
If I decomposed a website into its purpose, messages, and components, did that solve the loss of control from handing everything to a model?
I learned that decomposition helped most when its results became persistent, editable state. Passing the findings back as a larger prompt still left broad control with the model. Code could instead preserve the layout while allowing only copy to change. I also separated human control over rules from needing a person to supervise every run.
What practical techniques did the cookbooks add to that idea?
I found examples where code gathered candidates and a model selected an existing item instead of regenerating its value. Other examples separately checked whether an answer existed or a user had actually specified a setting. Those checks helped prevent the nearest candidate from becoming a false answer, or silence from becoming an invented preference. These were documented patterns, not experiments I had rerun.
I learned to separate model judgments from the code that stored state and chose actions. A more detailed prompt still left the model in charge; explicit state and rules made parts of the process inspectable and testable.
I came away seeing narrow AI judgments as parts of a program, with explicit state and rules around them.
“Help me decompose an AI workflow into observable inputs, bounded judgments, persistent state, and actions controlled by code. Show where generation is still needed and how to handle unknown answers.”
Why I askedI wanted to understand how speed, energy use, and a changing range estimate related to each other.
What I learnedI learned that air resistance rose quickly at highway speeds. Lower speeds could extend range, but the gain depended on conditions. A steady arrival estimate could already reflect adaptation to lower energy use.
Why I askedI wanted to understand what isolated desktops actually solved. I followed the idea through the choice of hardware and operating system.
What I learnedI learned to separate isolation, capacity, supervision, and scheduling. Two agents could have separate desktops and still conflict while changing the same website account.
Why I askedI wanted earlier experiments to improve later advice. I had been thinking about what an assistant should remember as its models became more capable.
What I learnedI learned that a useful memory needed links from an idea to an action, an observed result, and a later decision. Saving more notes did not close that loop, and missing measurements could not be treated as failed experiments.