Why a blog?
We learn alongside the teams who run their evaluation programs on EvalOps. This space lets us share the patterns that keep shipping responsible AI practical—and highlight the places we are still learning.
Building AI systems that work reliably in production is fundamentally different from traditional software engineering. When your application uses large language models, the same input can produce different outputs. Edge cases emerge that you never tested. Quality degrades silently as user behavior shifts or providers update their models. Traditional monitoring—uptime, latency, error rates—tells you the system is running, but not whether it's producing good results.
This gap between "the system works" and "the system is helpful" is where most AI projects struggle. You ship a feature that performs well in development, only to discover weeks later that users are frustrated, support tickets are rising, or worse—nobody noticed the degradation at all until someone manually reviewed outputs.
The Evaluation Gap
Most teams building with LLMs follow a familiar pattern:
- Write a prompt or train a model
- Test it on a handful of examples
- Think "this looks good"
- Deploy to production
- Hope for the best
When problems arise—and they always do—the debugging loop is painful:
- Users report "weird responses" but can't articulate exactly what's wrong
- You can't reproduce issues because of non-determinism
- You make changes based on intuition, not data
- Each "fix" might improve one case but break another
- You're constantly firefighting instead of systematically improving
This is evaluation theater: going through the motions of testing without rigorous measurement. It feels productive but doesn't actually de-risk production deployments.
The alternative is evaluation-driven development: treating evaluation as a first-class engineering discipline. Every change is measured against benchmarks. Every deployment includes quality gates. Every production interaction generates telemetry that feeds back into your evaluation datasets.
That's what EvalOps is built for—and what this blog documents.
What We're Learning About Evaluation
Over the past year working with teams shipping AI in production, we've seen consistent patterns:
Pattern 1: Evaluation complexity scales with application complexity
Early on, teams think they need simple accuracy metrics: "Did the model give the right answer?" But as applications mature, they need multi-dimensional scorecards: accuracy, safety, brand alignment, cost efficiency, latency, user satisfaction. Each dimension requires different measurement techniques—some automated, some requiring human judgment, some needing domain-specific validators.
Pattern 2: The best eval sets come from production failures
Your carefully curated test cases are useful, but they're not comprehensive. The edge cases that break your system in production are the ones you didn't think to test. Successful teams continuously harvest production traces—especially failures and low-scoring interactions—and add them to evaluation datasets. This creates a virtuous cycle: production informs testing, testing improves production.
Pattern 3: Speed matters more than perfection
Teams often spend weeks building perfect evaluation frameworks before running their first test. Meanwhile, competitors are iterating daily with "good enough" metrics. The goal isn't perfect measurement—it's fast feedback loops. Start with simple automated scorers (even if they're only 70% accurate), run evals on every commit, and refine your metrics as you learn what matters.
Pattern 4: Governance is evaluation at organizational scale
Once you have multiple teams building AI features, evaluation becomes a governance tool. Which models are approved for which use cases? What quality thresholds must features meet before launch? Who can override safety checks? These questions require centralized evaluation infrastructure, shared scorecards, and auditability—not just individual teams running ad-hoc tests.
Pattern 5: Cost and quality trade off in non-obvious ways
Switching from GPT-4 to GPT-3.5 might cut costs 95% but only reduce quality 10%—a great trade for high-volume, low-stakes applications. But you don't know until you measure. Multi-model evaluation strategies let teams optimize the cost-quality frontier: use expensive models where quality is critical, cheap models where good-enough is sufficient, and dynamically route based on input complexity.
What to Expect from This Blog
We're committed to sharing what we're learning—both from building the platform and from teams using it. You'll find:
Deep Technical Dives
How to build evaluation pipelines, design scorecards, implement LLM-as-judge patterns, optimize prompts systematically, evaluate RAG systems, and more. We won't just describe problems—we'll share concrete implementation patterns with code.
Product Updates with Context
When we ship a capability, we'll explain the "why" behind it. What problem were teams experiencing? What approaches did we try? Why did we choose this solution? Understanding the reasoning helps you apply patterns to your own challenges.
Patterns from the Field
We see teams solving similar problems in different ways. Some patterns emerge as clearly superior; others depend on context. We'll document both the successes and the missteps (including our own) so you can learn from collective experience.
Grimoire Community Edition Updates
Grimoire is our free, open-source telemetry agent. It captures evaluation traces locally or sends them to EvalOps for centralized analysis. We'll share how teams are using it, new capabilities we're adding, and integration patterns with existing tools (LangChain, LlamaIndex, CI/CD systems).
Measurement Philosophy
Evaluation isn't just a technical problem—it's a philosophical one. What does "quality" mean for your application? How do you balance competing metrics? When should you prioritize user satisfaction over automated scores? We'll explore these questions and share frameworks for thinking about measurement in ambiguous domains.
Who This Blog Is For
AI Engineers building production systems need evaluation infrastructure as much as they need model APIs. If you're tired of debugging by vibes and want data-driven iteration, this blog will help you build systematic evaluation practices.
ML Engineers optimizing models and prompts will find patterns for measuring improvements, running A/B tests, detecting regressions, and confidently deploying changes without manual spot-checking.
Platform Teams responsible for AI infrastructure need to provide evaluation capabilities to product teams. We'll cover architecture patterns, multi-tenancy considerations, and how to build shared evaluation resources that multiple teams can leverage.
Product Teams shipping AI features need to understand quality tradeoffs. What does "good enough" look like? How much does better quality cost? When should you ship versus iterate more? Quantitative evaluation makes these decisions explicit rather than implicit.
Leaders navigating AI governance need frameworks for setting quality standards, managing risk, and ensuring teams aren't shipping unsafe or unreliable systems. Evaluation is the foundation of responsible AI at scale.
The Road Ahead
We're in the early days of figuring out how to build reliable AI systems. Traditional software engineering practices—testing, monitoring, CI/CD, observability—need to evolve for non-deterministic systems. The patterns are still emerging.
Some things we're actively exploring:
Adaptive evaluation: How can eval sets automatically expand based on production failures? Can we detect when our benchmarks become outdated?
Cross-model evaluation: As teams use multiple models (GPT-4, Claude, Llama, Gemini), how do you maintain consistent quality definitions across different providers?
Evaluation efficiency: Running comprehensive evals on every commit gets expensive. How do you balance coverage with speed and cost?
Human-AI collaboration in evaluation: Some quality dimensions require human judgment. How do you efficiently incorporate human feedback into automated pipelines?
Evaluation for agentic systems: When AI systems make multi-step decisions, use tools, and interact over long sessions, traditional evaluation breaks down. What does "correctness" mean for an agent?
We don't have all the answers yet. That's why this blog exists—to share what we're learning, document what's working, and be honest about what's still unsolved.
Get Involved
This isn't a one-way broadcast. The best insights come from practitioners in the field:
Share your patterns: If you've solved an evaluation challenge, we'd love to document it (with credit, of course). Email us at hello@evalops.dev.
Request topics: What evaluation problems are you struggling with? What would you like to see covered? Let us know.
Join the community: We're building a community of teams serious about evaluation. Share your approaches, learn from others, and collectively raise the bar for AI quality. Join the discussions on GitHub.
Try the tools: EvalOps and Grimoire exist to make evaluation practical, not theoretical. Install Grimoire, capture your first traces, and see what systematic evaluation looks like.
What's Next
Our next posts will dive into specific technical patterns:
- Hardening evaluation telemetry: How to ensure trace capture is accurate, complete, and trustworthy across environments
- Building domain-specific scorecards: Moving beyond generic accuracy to measure what actually matters for your application
- Evaluation gates in CI: Blocking deployments when quality regresses, with practical GitHub Actions examples
- Multi-model evaluation strategies: Systematically comparing GPT-4, Claude, open-source models, and building intelligent routing
- RAG evaluation patterns: Measuring retrieval quality, grounding, and end-to-end answer correctness
We'll also share product updates as we ship new capabilities, and spotlight interesting patterns from teams using EvalOps in production.
Stay Connected
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- Twitter: @evalops for post announcements and quick insights
Or just check back here regularly. We're publishing weekly as we document the patterns emerging from production AI deployments.
Thanks for joining us on this journey. Let's build AI systems we can actually trust.
Questions or feedback? Email hello@evalops.dev or join the community discussions.