Swapping AI models rarely fixes bad output. The context you feed it does more work than people realize.

Noticed a pattern: people switch from GPT to Claude, upgrade to a newer version, try a bigger model and the output barely changes. If that's happened to you, the issue usually isn't the model. It's what you handed it before asking the question. Broke it down to three things context actually needs to supply, and most disappointing outputs are missing one of these, not all of them: Current facts the training data can't know: your pricing, this quarter's numbers, a customer's actual history. Leave this out and the model doesn't leave a blank, it quietly invents something plausible. A concrete example of what "good" looks like: not "professional tone," an actual paragraph to pattern-match against. Descriptions get interpreted, examples get copied. What already happened earlier in the task: a correction you made two messages ago. If you don't restate it, it's gone. The model isn't ignoring you, it just doesn't re-read messages you haven't pointed it back to. The counterintuitive part: the most common mistake isn't giving too little context, it's dumping in too much unfiltered. The model has to weigh every token, and irrelevant material competes for attention with what actually matters. Forty pages when the task needs three paragraphs makes the right answer harder to find, not easier. Wrote up a longer breakdown with a concrete before/after example (same task, same model, only the context changed): https://medium.com/@nagatomopedro05/good-ai-starts-with-good-context-design-77496f7b9eb6 Curious if others here have run into this, model-swapping as a first instinct instead of fixing the input. submitted by /u/ClickOk5811 [link] [留言]