Engineering Effective AI Evaluations: Lessons from Production LLM Deployments

Building robust AI applications requires more than good prompts—it demands systematic evaluation frameworks that enable rapid iteration and continuous improvement. Organizations deploying LLMs at scale have learned hard lessons about what separates effective evaluation systems from academic exercises.

Three Signs Your Evaluations Actually Work

Your evaluation system succeeds when it enables three critical capabilities:

24-Hour Model Updates: When a new model releases, you should deploy an updated product within 24 hours. Companies like Notion achieve this consistently because their evaluations provide confidence in model performance before deployment. If you can’t update quickly, your evaluation system needs work.

User Feedback Integration: You need a clear path from user complaints to evaluation improvements. When users report issues, you should easily convert their feedback into test cases. Without this pipeline, valuable user insights disappear.

Offensive Capability Assessment: Use evaluations to understand which use cases you can solve before shipping features. Unlike unit tests that catch regressions, effective evaluations predict success rates for new capabilities.

Engineering Evaluations, Not Just Running Them

Great evaluations require engineering investment—they don’t emerge from synthetic datasets and generic scoring functions.

Build Living Datasets

No dataset perfectly represents reality. The best datasets continuously evolve as you learn from production usage. This requires treating your dataset as an engineering problem, not a static resource.

Most real-world use cases differ significantly from pre-built datasets. Competition math problems work well with existing benchmarks because they’re well-defined. Business applications need custom evaluation data that reflects actual user behavior.

Write Custom Scoring Functions

Every sufficiently advanced organization writes custom scoring functions. Think of scorers as specifications for your AI application—using generic scorers means implementing someone else’s requirements, not yours.

Generic scoring functions serve as starting points, but production systems need scorers that encode your specific quality standards and business requirements.

Context Dominates Modern Prompts

Traditional prompt engineering focused on system prompts. Modern AI applications use complex context that extends far beyond initial instructions.

Agent-based systems typically follow this pattern:

1. System prompt
2. Loop of LLM calls
3. Tool executions
4. Context integration
5. Iteration

Analysis of production agents reveals that system prompts represent only 15-20% of total tokens. Tool definitions, responses, and conversation history dominate the context window.

Design Tools for LLMs, Not APIs

Don’t simply expose existing APIs as tools. Design tool interfaces specifically for LLM consumption:

• Tool Definitions: Write them like prompts—clear, specific, and optimized for model understanding
• Output Formats: Choose formats that LLMs process efficiently (YAML often works better than verbose JSON)
• Response Structure: Design outputs for analysis, not just programmatic consumption

Prepare for Model Disruption

New models can transform impossible use cases into viable features overnight. Engineer your systems to capitalize on these opportunities immediately.

One production example shows dramatic capability improvements across model generations:

• GPT-4: 10% success rate (not viable)
• GPT-4 Turbo: 12% success rate (still not viable)
• Claude 3.5 Sonnet: 35% success rate (viable)
• Claude 3.5 Opus: 45% success rate (strong performance)

This feature shipped two weeks after Claude 3.5 Sonnet’s release because the evaluation system was ready to test new models immediately.

Create Ambitious Evaluations

Build evaluations for capabilities that don’t work with current models. When new models release, you’ll immediately know if previously impossible features become viable.

Use model-agnostic tooling so you can test new models without code changes. This enables same-day assessment of new releases.

Optimize the Entire System

Prompt optimization alone produces limited improvements. Optimize your complete evaluation system: data, tasks, and scoring functions together.

Testing shows dramatic differences between approaches:

• Optimizing prompts only: Minimal improvement
• Optimizing prompts + data + scoring: 10% → 45% success rate

This system-wide optimization transforms unviable features into production-ready capabilities.

Implementation Strategy

Start with these concrete steps:

1. Audit Current Capabilities: Can you deploy model updates in 24 hours? Do you convert user feedback into test cases?

2. Build Custom Components: Replace generic datasets and scorers with custom versions that reflect your specific requirements

3. Design LLM-Optimized Tools: Rewrite tool interfaces for model consumption, not API compatibility

4. Create Future-Ready Evaluations: Build tests for ambitious use cases that current models can’t handle

5. Implement Holistic Optimization: When improving performance, modify data, prompts, and scoring functions together

The evaluation landscape is evolving rapidly as LLMs become capable of automatically improving prompts, datasets, and scoring functions. Organizations that build robust evaluation systems now will be positioned to capitalize on each new model release, turning AI advancement into immediate competitive advantage.