Tests and Evals

LLM Evals and the Analogy with Source Code Tests

Drawing the Analogy

In software engineering, tests are the backbone of reliability. Unit tests validate small functions, integration tests check system interactions, and regression tests ensure new changes don’t break old functionality. In the world of large language models (LLMs), evals serve a parallel role:

• Unit tests → targeted evals (e.g., math reasoning, factual accuracy, safety checks).

• Integration tests → scenario evals (e.g., multi-turn conversations, complex workflows).

• Regression tests → benchmark evals (ensuring new model versions don’t degrade on established tasks).

Both are about trust: tests build trust in code, evals build trust in models.

Startups: Speed Over Discipline

Early-stage startups often skip tests entirely. Their priority is shipping features quickly, validating product-market fit, and iterating fast. Writing tests feels like a slowdown when survival depends on velocity. Similarly, in today’s LLM landscape:

• Startups often rely on ad hoc prompt testing rather than systematic evals.

• Demos and prototypes are prioritized over rigorous evaluation frameworks.

• Evals are seen as overhead, not infrastructure.

This raises a risk: just as untested codebases collapse under scale, unevaluated LLMs can fail spectacularly when deployed to real users—producing biased, unsafe, or unreliable outputs.

Large Tech Companies: Tests as Infrastructure

In mature engineering organizations, tests are non-negotiable:

• Code cannot be merged without passing automated test suites.

• Continuous integration pipelines enforce reliability.

• Teams are measured not just on speed, but on stability.

The same pressures are emerging for LLMs in large companies:

• Scale magnifies risk: a flawed model deployed to millions can cause misinformation or reputational damage.

• Regulatory scrutiny: governments are beginning to demand transparency and accountability in AI systems.

• Enterprise trust: customers expect guarantees of reliability, fairness, and safety.

For these reasons, evals are likely to become mandatory infrastructure in large-scale LLM deployments.

Will Evals Gain Momentum?

The trajectory mirrors the evolution of software testing:

• Startups will continue to skip evals in the early stages, focusing on speed.

• Large companies will institutionalize evals, building them into CI/CD pipelines for models.

• Industry standards will emerge, much like test coverage metrics in software engineering.

• Regulation and competition will accelerate adoption, making evals not just best practice but a requirement.

Conclusion

LLM evals today are where software tests were two decades ago: undervalued in startups, but increasingly critical in large organizations. Over time, evals will evolve from “nice to have” to essential infrastructure, enforced by scale, regulation, and trust requirements. The question isn’t whether evals will gain momentum—it’s how quickly the industry will recognize them as the backbone of responsible AI deployment.