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AI Feature

What is an AI feature?

An AI feature is a specific capability or functionality within a product that uses artificial intelligence (typically LLMs or machine learning) to solve a customer problem or deliver value. AI features differ from traditional product features in fundamental ways—they require prototyping with LLMs, evaluating non-deterministic outputs through evals, and ongoing maintenance to ensure quality and accuracy over time.

How do AI features differ from traditional features?

Traditional software features can be thoroughly tested with unit tests and integration tests. Once those tests pass, teams can trust the feature will continue working as expected. AI features work differently because they're non-deterministic—the same input might produce different outputs. This means teams must continuously monitor and evaluate AI features even after launch.

Additionally, AI features require different success criteria. While traditional features either work or they don't, AI features must meet quality thresholds. A feature might work technically but still need to be rolled back if it doesn't achieve sufficient accuracy for user expectations, as Stack Overflow discovered with their conversational search feature that didn't reach the 70% accuracy developers required.

What maintenance do AI features require?

AI features need continuous evaluation to ensure they continue delivering quality outputs. Unlike traditional features where passing tests means ongoing confidence, AI features require regular monitoring, customer feedback loops, and periodic re-evaluation. Teams must factor in this "forever maintenance cost" when deciding whether a problem is worth solving with AI. The value delivered must justify the ongoing investment in quality assurance and monitoring.

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