Lessons from Implementing an Eval-Driven Development Framework with My Team

Building an eval-driven development framework is an exciting journey, filled with opportunities to rethink the way we build and iterate on our AI models. This blog post dives into the process my team undertook to implement an eval-driven development framework, sharing the lessons learned and the key practices that emerged along the way.

Lesson 1: The Power of Continuous Evals over Gut Instincts (and Vibes)

The foundation of our new framework was shifting away from ad-hoc assessments of AI quality to systematic, continuous evaluation—“evals.” Traditional software engineering relies heavily on deterministic unit and integration tests, but AI models are inherently probabilistic, and this makes predictability a challenge. To tackle this, we created a combination of automated, human, and LLM-based evals that help keep our expectations on track and ensure our models meet defined quality standards consistently.

What we learned was that gut feelings often introduce bias, and it’s easy to either overlook subtle problems or fall into over-correction. Evals introduced structure: data-driven evaluations provided us with clarity and a concrete way to iterate. Initially, switching to this systematic approach felt slower, but within the first month, the benefits were evident. The consistency of our evaluations and the data transparency they brought were crucial in identifying and preventing regressions we wouldn’t have caught before.

Lesson 2: Integration into CI/CD Enforces Accountability

One of our early wins was integrating evals into our CI/CD pipelines. By treating evals as quality gates, we raised the bar on what it meant to deploy code. Our models had to meet certain criteria before they moved to production. This integration ensured continuous quality and meant that any regression would automatically halt deployment.

This kind of accountability pushed us towards better collaboration. Developers took the initiative to understand evaluation criteria more deeply, and our team began to share feedback more actively—not just to improve AI outputs but also to refine the grading processes themselves. The integration fostered a culture where quality was owned by everyone, not just a specific team.

Lesson 3: Balancing Automation and Human Grading

We quickly learned that there’s no one-size-fits-all approach to evaluation. Automated grading was perfect for objective, clear-cut criteria—like ensuring specific keywords were present or checking for syntax correctness. However, more nuanced aspects of quality, like creativity or coherence, benefited from human judgment.

Yet, human grading introduced variability. We dealt with this by creating a detailed grading rubric and training our evaluators, resulting in reduced subjectivity and greater consistency. Interestingly, adding LLM-based evals—using other models to score our AI outputs—helped us scale human-like evaluation without the full cost of manual graders. Balancing these three evaluation types allowed us to maintain cost efficiency while not compromising on quality.

Lesson 4: Continuous Improvement Is Not Just a Feature, It’s a Mindset

Evaluations were just the start. We set up dashboards to provide real-time feedback and track performance metrics. The idea was to turn eval results into actionable insights. Every team member could see what was working and where the system was struggling. This real-time awareness shifted our mindset from “deploy and forget” to “deploy and grow.”

The integration of these feedback loops into our workflows created a new level of transparency and accountability. It made our improvement cycle explicit—not just about fixing bugs but also about refining the criteria by which we judged our work. Over time, the results spoke for themselves: we saw a measurable increase in eval pass rates and far fewer regressions.

Key Practice Improvements

  1. Set Up Grading Rubrics Early: The subjectivity of human evaluators can be a challenge. Developing grading rubrics in advance ensured consistency across evaluations and helped us onboard new evaluators quickly.

  2. Automate Whenever Feasible, But Embrace Human Nuance: Automated evals are great for rapid, repeatable assessments, but human evaluators still offer a depth that automation lacks—especially for evaluating user-facing content quality.

  3. Incorporate Evals into CI/CD as Quality Gates: Continuous evaluation not only catches problems but also prevents them from reaching users, acting as a reliable quality filter.

  4. Create Feedback Dashboards to Foster Transparency: Providing every stakeholder with access to evaluation metrics ensures everyone is aligned and working towards the same goals.

Final Thoughts

Implementing an eval-driven development framework wasn’t without its challenges, but it was a transformative experience. It allowed our team to bring more objectivity, consistency, and collaboration into the AI development process. The blend of automated, human, and LLM-based evals let us scale our efforts while keeping quality at the core. We still have open questions, like how best to further standardize human grading or how to incorporate evolving criteria over time, but what’s clear is that the path we’re on is leading us towards better outcomes, more reliable models, and a culture that prioritizes continuous improvement.