Without Evals, AI Development Moves Slower
Why proper evaluation systems are essential for maintaining development velocity as AI applications grow in complexity
You've successfully launched your AI app. The initial features work beautifully. Then something strange happens: the last few things take forever to build.
Sound familiar? You're not alone. This is becoming common as companies scale their AI applications beyond prototypes. A common culprit is the absence of proper AI evaluation systems.
The exponential complexity trap
Let's consider a concrete example. You've built an AI system that scans receipts and generates tax-deductible records. It works for basic expenses like fuel, office supplies, and meals. Now your product team wants to add personalisation: "Can we make the system more specific based on whether the user is a sole trader or a company?"
You change the prompt to include user context. The system now provides more detailed categorisations for sole traders versus companies. Great! But during testing, you discover that fuel receipts are no longer classified as deductible expenses.
So you fix the fuel classification issue. Now, depreciation calculations break. You fix depreciation, but meal expense categorisation becomes inconsistent. Each fix introduces new regressions, and the time to put in place each feature grows exponentially.
This is the mathematical reality of building complex systems without proper quality gates. As your AI application gains features, the testing surface area grows. Every change needs a manual check to ensure that all previous functions still work.
Why multi-agent systems don't solve everything
Many teams try to escape this complexity trap by splitting their monolithic AI system into many specialised agents. While this approach can help, it doesn't eliminate the fundamental problem. Instead, it redistributes it.
Consider a tax preparation system. Even if you split it into separate agents for different tax categories, each agent still needs to handle many rules and edge cases. The depreciation agent alone must understand different asset types, varying depreciation schedules, and complex legal requirements. As you add more tax law coverage, the single agent faces the same exponential complexity growth within a narrower domain.
The manual testing bottleneck
Without evaluation systems, teams rely on manual testing. A developer or subject matter expert runs through a handful of test cases, verifies the output looks reasonable, and ships the feature.
But manual testing has a flaw: it doesn't scale with complexity. As your AI system grows, comprehensive testing requires checking a growing matrix of scenarios. The tester gets "evaluation fatigue" and starts overlooking small issues. Users will notice these problems right away.
Here are the warning signs that manual testing has become your bottleneck:
- "Why is this broken when it used to work?"
- "How come this last feature is taking so long to build?"
- "Our AI engineers seem to be taking forever on simple changes..."
- "We keep finding bugs that should have been caught before release..."
Understanding AI evaluation systems
An evaluation system is any automated process that provides a pass/fail assessment of your AI's performance. Think of it as the AI equivalent of unit tests, but designed for non-deterministic systems.
There are two categories of evaluations, and successful AI applications need both:
Deterministic Evaluations test objective, measurable outcomes:
- Was the correct function called?
- Does the response follow the required format?
- Was the database updated?
- Does the output contain the required information?
These evaluations leverage familiar testing patterns that software engineers already understand. You can implement them using standard testing frameworks, checking function calls, database states, and response structures.
Non-Deterministic Evaluations assess subjective qualities that need judgement:
- Does the response use an appropriate tone for customer service?
- Did the AI follow the correct process (e.g., gathering information before providing recommendations)?
- Is the explanation clear and helpful for the target audience?
- Does the response maintain consistent personality across interactions?
For these evaluations, you need systems that can make judgment calls. These can either be humans following structured evaluation criteria (say, a test plan) or other AI models trained to assess quality. These are often referred to as "LLM-as-Judge" systems.
Addressing the "too complex" misconception
Many teams believe that building evaluation systems for AI is complex or expensive. This misconception stems from assuming you need sophisticated infrastructure from day one.
The reality is that effective evaluation can start simply. Begin with human evaluation: create a set of 50 representative conversations, run your AI system against them, and have a subject matter expert review each response. This approach is great for teams that use waterfall development cycles.
As your application matures, you can introduce LLM-as-Judge systems, where one AI model evaluates the outputs of another. The key is measuring alignment. This is all about ensuring your evaluation model agrees with human judgment at least 90-95% of the time before trusting it to run autonomously.
Basic evaluation systems offer quick benefits. They catch regressions before users see them. This helps teams feel confident to quickly iterate on prompts and features.
What changes with proper evaluation
When you install comprehensive evaluation systems, the development experience transforms fundamentally.
Linear Development Velocity
Feature development shifts back to a steady, linear effort, avoiding exponential slowdowns. You're no longer spending increasing amounts of time verifying that existing functionality still works.
Production Monitoring
The same evaluation systems that test your development builds can track live conversations. You can detect quality degradation in real-time, set up alerts for patterns, and maintain quality standards even as your system serves thousands of users.
Deployment Confidence
Automated evaluation gives teams the confidence to deploy changes frequently. Instead of lengthy manual verification cycles, you can trust your evaluation suite to catch issues before they impact users.
Advanced Capabilities
With a strong evaluation system, you can add features like security monitoring (to spot adversarial prompts), compliance checks (to ensure responses meet regulations), and quality dashboards that track performance trends over time.
Think of evaluation systems like a supervisor for your AI employee. They help ensure the AI keeps professional standards, follows the right steps, and delivers steady quality. Except this supervisor never gets tired, works 24/7, and scales forever.
The competitive advantage
Companies that master AI evaluation systems gain a significant advantage over those stuck in manual testing cycles. They ship features faster, with higher quality, and have greater confidence in their production systems. Competitors face high development costs, but teams with proper evaluation keep a steady pace. This allows them to iterate quickly.
The teams that recognise this pattern early and invest in evaluation infrastructure will dominate their markets. Those who continue relying on manual testing will find themselves unable to compete on development speed or deployment frequency.
At our consulting practice, we help companies break free from complexity. We design evaluation systems that fit your unique needs and goals. If you're facing slowdowns in development or want to build evaluation systems, we can help. Let's talk about how systematic AI quality assurance can speed up your development process.
Email us at: hello@verticalai.com.au
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