mispriced retweeted
The next big advantage in AI won’t come from writing better prompts.
It will come from building better systems around the models.
Think about AI coding agents. Instead of repeatedly asking them to write cleaner code, you can define project-specific rules, give them access to the right tools, provide examples of acceptable output, and automatically test their work.
The difference is significant: you’re no longer relying on a single instruction to produce the right result every time.
You’re creating an environment where the model has the context, constraints, and feedback it needs to work more reliably.
The real skill is shifting from telling AI what to do to designing systems that help it consistently do the right thing.
prompt engineering is starting to feel like legacy terminology.
the model isn’t really being steered by one prompt anymore. in a good ai system, behavior gets distributed across layers:
> rules
> skills
> tools
> examples
> schemas
> evals
the prompt is just one control surface.
instead of telling a model to write good code, you encode repo rules, define when tools should be used, show examples of acceptable output, constrain responses with schemas, then run evals to see if the whole thing actually works.
that’s the shift: less wordsmithing, more environment design.
the gap between good and bad ai users probably won’t come down to who writes the cleverest prompt. it’ll come down to who builds the better environment around the model.
prompt engineering is shrinking.
system design is becoming the actual skill.
mispriced retweeted
the bigger consequence is that company knowledge has to become executable.
today a lot of important judgment still lives in people's heads:
- when to escalate.
- what counts as risky.
- which exceptions are acceptable.
- how quality gets checked.
- which tools are allowed.
if agents are going to do real work, that knowledge can't stay tribal.
it has to get encoded into rules, tools, schemas, evals and approval boundaries.
prompt engineering was mostly about communicating with a model.
the next skill is turning how a business operates into an environment a model can reliably act inside.
prompt engineering is starting to feel like legacy terminology.
the model isn’t really being steered by one prompt anymore. in a good ai system, behavior gets distributed across layers:
> rules
> skills
> tools
> examples
> schemas
> evals
the prompt is just one control surface.
instead of telling a model to write good code, you encode repo rules, define when tools should be used, show examples of acceptable output, constrain responses with schemas, then run evals to see if the whole thing actually works.
that’s the shift: less wordsmithing, more environment design.
the gap between good and bad ai users probably won’t come down to who writes the cleverest prompt. it’ll come down to who builds the better environment around the model.
prompt engineering is shrinking.
system design is becoming the actual skill.