Shipping real AI features without the hype
Where LLMs genuinely earn their place in a product, how to keep them reliable, and the guardrails that keep costs and hallucinations in check.
An AI feature that ships is worth more than ten in a backlog. The trick is picking tasks where a language model is genuinely better than a form — and engineering around its weaknesses.
Pick the right task
LLMs shine at turning messy human input into structure: summarizing, extracting, classifying, and drafting. They struggle with exact arithmetic and anything needing a single correct answer. Match the feature to the strength.
Make it reliable
Constrain the output: force JSON with a schema and validate it, retrying on mismatch. Ground it by giving the model the data it needs in-context instead of trusting its memory. And verify — for anything important, have a second pass, or a human, check the result.
Control the cost
Cache aggressively, batch where you can, and use a smaller model for the easy cases. Most "AI is too expensive" problems are really "we sent the whole document on every keystroke" problems.
Pick the right task
LLMs shine at turning messy human input into structure: summarizing, extracting, classifying, and drafting. They struggle with exact arithmetic and anything needing a single correct answer. Match the feature to the strength.
Make it reliable
Constrain the output: force JSON with a schema and validate it, retrying on mismatch. Ground it by giving the model the data it needs in-context instead of trusting its memory. And verify — for anything important, have a second pass, or a human, check the result.
Control the cost
Cache aggressively, batch where you can, and use a smaller model for the easy cases. Most "AI is too expensive" problems are really "we sent the whole document on every keystroke" problems.
