@Mikocrypto11: Most people think prompting is just about writing better sentences. This 32-minute workshop from Anthropic talks about something else: why that's not enough. It doesn't tear apart prompt hacks, or "act as", or another $300 course…

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Summary

Anthropic's 32-minute workshop emphasizes that good prompts are not single-sentence tricks, but a testable, improvable workflow involving goal setting, boundary definition, self-checking, and edge case handling.

Most people think prompting is just about writing better sentences. Anthropic’s 32-minute workshop talks about something else: Why that's not nearly enough. It doesn’t tear apart prompt hacks. Not “act as.” Not another $300 course. It’s about how prompts are actually built in production. Evals. Edge cases. Model migration. Agent loops. A good prompt isn't a single sentence. It’s a workflow the model can understand, execute, test, and improve upon. This is important. Because whether it’s the new Claude Opus or the new Sonnet, the truly effective rules are probably still these: - Let the model know the goal. - Let the model know the boundaries. - Let the model know how to check itself. - Let the model know what to do when it encounters an edge case. So a lot of the time, a bad answer from AI isn’t necessarily because the model is bad. It might just be because the setup is bad. This prompting playbook: recommend reading it fully before blaming the AI next time.
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Cached at: 06/02/26, 07:37 PM

Most people think prompting is just about writing better sentences.

Anthropic’s 32-minute workshop tells a different story: Why that’s nowhere near enough It doesn’t break down prompt hacks Not “act as” Not another $300 course But how prompts are actually built in production

Evals
Edge cases
Model migration
Agent loops
A good prompt isn’t just a sentence.

It’s a workflow that a model can understand, execute, test, and improve. This matters a lot.

Because no matter what comes next — a new Claude Opus or a new Sonnet — the rules that actually hold are probably still these:

  • Let the model know the goal
  • Let the model know the boundaries
  • Let the model know how to check itself
  • Let the model know what to do when it hits an edge case

So a lot of the time, a bad answer from AI isn’t necessarily because the model is poor. It could just be that the setup is poor. This prompting playbook — I’d suggest reading it before you blame the AI next time.

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