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AI 资讯

How to Add Evals to an LLM Feature

Learning how to add evals to an LLM feature is the difference between shipping a demo and shipping a reliable product. When you embed an LLM into a real feature — a chatbot, a voice agent, a document summarizer — you’re not just calling a model. You’re betting your user’s experience on a non‑deterministic system that can silently break with every prompt tweak, model update, or edge case. That’s why we instrument every LLM feature we build with a purpose‑built eval suite. Here’s how we did it for an outbound AI calling agent and how you can do the same. Why Evals Are Not Optional LLMs are non‑deterministic: give them the same input twice, and you’ll get two different responses. That means unit tests that check for exact string matches are useless. As Pragmatic Engineer notes , you need evals to verify that the solution works well enough — because there’s no guarantee it will. When you’re building a feature that speaks to real customers, like the AI Calling Agent dashboard we built, a regression in tone or missed booking intent can cost revenue immediately. Evals turn that uncertainty into signal. How to Add Evals to an LLM Feature: A 4‑Step Workflow We’ll walk through the exact process we followed, from defining success to automating checks in CI, using the DeepEval framework as an example. You can swap in Evidently AI or build your own, but the pattern is the same. Step 1: Define Success for Your Feature Takeaway: Before you pick a metric, write down the one thing that makes the feature “done” — usually a business outcome, not a technical measure. For the AI Calling Agent, the core feature was an outbound call that books a meeting. The success criterion wasn’t “the LLM replied politely.” It was “the agent scheduled a meeting with the right time and date.” This is a reference‑based evaluation: you compare the output to a known ground truth. Evidently AI’s guide calls this pattern out as essential for regression testing and experimentation. From that criterion, we der

2026-07-11 原文 →
产品设计

Range anxiety

The storm developed quickly over west central Illinois on April 17th, first as a single high-intensity system called a supercell, and then later that evening transforming into a long squall line of thunderstorms. Tucked inside a wall of wind were several smaller, quick-forming tornadoes, one of which landed directly on Rivian’s electric vehicle factory on […]

2026-07-01 原文 →
科技前沿

The $400 million machine powering the future of chipmaking

Jos Benschop is climbing a ladder to get to the top of his newest machine. It’s a bit of a schlep. The contraption is the size of a double-decker bus—more than 150 tons of gleaming precision-milled aluminum covered in thousands of snaking tubes, colored cables, and pressurized tanks. From the ground, it looks like a…

2026-06-23 原文 →
产品设计

Inside the world’s deepest and longest subsea road tunnel

It’s cold, it’s very, very noisy, and—if I can be quite honest with you—I’m not feeling super relaxed. I’m currently around 300 meters, or 1,000 feet, beneath the North Sea, in a dark, dank cave. It smells weird. And I am increasingly aware of the pressure from millions of tons of seawater just above my…

2026-06-22 原文 →
AI 资讯

The search for dark matter has been blown wide open

Underneath an Apennine massif, below the Jinping Mountains of Sichuan, and at the bottom of a South Dakota mine, there is a cosmic hunt afoot. Isolated deep beneath these rocky shields, massive detectors filled with liquid xenon aim to make the first direct detections of dark matter, the long-sought invisible substance whose gravity has sculpted…

2026-06-18 原文 →