Garbage in, confident nonsense out: feeding context to an LLM

Everyone obsesses over which model to use. The quiet truth of building useful AI features is that your context matters more than your model.

If you build one AI feature, you'll learn this fast: the model is only as good as what you feed it. A bigger model can't save you from messy input — it'll just produce more fluent nonsense. The unglamorous lever almost nobody talks about is context quality.

Structure beats volume

Dumping an entire scraped HTML page into the prompt is the worst possible fuel — navbars, footers, ads, all competing for the model's attention. Clean, structured data (defined fields, clear types, real relationships) is the best. The same question with tidy context gets a dramatically better answer than with a giant blob, even on the same model.

Garbage context in, confident nonsense out. Most “the AI is dumb” problems are really “my input was a mess” problems.

A practical checklist

• Retrieve only what's relevant — fewer, sharper chunks beat the whole document.
• Strip boilerplate before it ever reaches the prompt.
• Give the model structure (headings, fields, labels), not soup.
• Tell it what to do when it doesn't know — “say you're unsure” prevents a lot of made-up answers.

Spend your energy on clean, well-structured context and the model part gets surprisingly easy. The future worth betting on isn't bigger models — it's better fuel flowing into them.