AI-Assisted Developer Tooling

Context

AI tooling is easy to prototype and hard to trust. A flashy demo that’s right 70% of the time can be worse than no tool at all, because engineers stop relying on it. The goal here was to bring LLMs into real engineering workflows in a way that earned and kept trust.

Approach

The work treated evaluation and guardrails as core features, not afterthoughts:

• Grounding over guessing. Retrieval-augmented generation anchored responses in real internal sources, so answers cited something concrete instead of hallucinating.
• Measured quality. Evaluation pipelines scored outputs against curated cases on every change, turning “it feels better” into a number we could defend.
• Guardrails by default. Inputs were validated, outputs were checked, and anything high-stakes kept a human in the loop.
• Budgets. Latency and cost ceilings were built in, because an AI feature that’s slow or expensive doesn’t survive contact with production.

Outcome

Engineers got tooling that removed genuine friction from repetitive work — and, just as importantly, tooling they could trust because its quality was measured and its failure modes were contained. Treating evaluation as a first-class concern is what separated a useful product from an impressive demo.