How to evaluate LLMs before production

The gap between a model that scores high on a leaderboard and one that actually works in the trenches is a chasm that many teams only discover after a costly production incident. GitHub's post on evaluating LLMs for secret scanning is a refreshing dose of reality in a sea of "AI will fix everything" marketing. They're not talking about vague vibes or demo videos—they're talking about precision and recall, false positives that drown your security team, and false negatives that let a real credential slip through. That's the unglamorous engineering work that separates a toy from a tool. What's particularly sharp here is the context: secret scanning is a high-stakes, low-tolerance task. A model that's 99% accurate might still be useless if that 1% error rate means a leaked AWS key goes unnoticed. GitHub's approach, as implied by their lessons, likely revolves around building a domain-specific evaluation set that mirrors the messy reality of codebases—not just clean, curated snippets from a benchmark. That's a lesson that applies far beyond security: any production LLM needs an eval set that reflects your actual data distribution, your edge cases, and your failure modes. This also underscores a broader trend in the industry: the shift from "Can this model do the thing?" to "How do we know this model does the thing reliably, at scale, with acceptable risk?" Evaluation is becoming a first-class engineering discipline, not an afterthought. The fact that GitHub is openly sharing these lessons is a win for the open-source community, but it also serves as a warning—if you're not investing in rigorous eval infrastructure, you're playing with fire. The source article is a must-read for anyone building on LLMs, not just for the specific secret-scanning details, but for the mindset it represents.