Ever seen AI generate the most outrageous response to a question, or produce outputs that are just plain meaningless or stupid? You're not alone. The rapid adoption of artificial intelligence has unveiled a critical challenge: How do we ensure these powerful systems consistently deliver accurate, reliable, and trustworthy results? The answer lies in proper testing of the AI's output as an integral part of the development and deployment process. These systematic assessments are called AI evaluations, or "evals."
At its core, an eval is a rigorous process designed to measure the performance, reliability, and quality of an AI system's outputs against a predefined set of criteria. Think of it as a crucial report card for your AI, but one that goes far beyond simply checking if it completed a task. It's about ensuring the AI's actions align precisely with your business goals, user expectations, and even ethical standards.
This meticulous assessment is, in fact, the backbone of product reliability for any AI-powered solution. In the fast-paced world of GTM automation-where AI interacts with prospects, writes messages, qualifies leads, or generates insights-the difference between a well-designed eval and a vanity metric is the difference between trust and guesswork. Evals provide the critical feedback loop necessary for continuous AI improvement, transforming subjective observations into objective data. Without it, developing and deploying AI is akin to navigating a ship without a compass – you might be moving, but you'll have no reliable way to confirm you're headed in the right direction, let alone whether you'll reach your destination safely. Evals provide that compass, offering the data-driven insights needed to refine models, optimize performance, and build genuine confidence in your AI investments.
But not all evals are created equal. Some questions are best answered with code, others with language models. Knowing when to use each saves time, improves accuracy, and ensures your AI solution remains grounded in measurable outcomes.
Code-Based Evals
What They Are
Code-based evals use deterministic checks-assertions or logic tests that can be computed without AI judgment. They’re fast, inexpensive, and best for problems with an objective right answer.
When to Use Them
Use code-based evals when the success condition is clear, binary, and testable.
Common GTM use cases:
- Data accuracy: Did the AI extract the correct date, phone number, or price from an email?
- Routing logic: Was a lead with a certain score assigned to the right rep or queue?
- Tool execution: Did the “schedule demo” tool actually trigger within two turns after the user asked for it?
- Response timing: Was a follow-up generated within the SLA window?
Example
assert extracted_date == expected_date
assert tool_called("handoff_to_human") == True
These assertions can run automatically in your deployment process or production logs-no human judgment required.
Why They Matter
- Cheap and scalable: Run across millions of interactions.
- Ground truth confidence: You either passed or failed.
- Excellent for regression tests: Great for catching breaking changes before release.
LLM-Based Evals
What They Are
LLM-based evals (a.k.a. “LLM-as-a-judge”) use another language model to rate or classify an AI’s output. They’re ideal when success is subjective or contextual-when “right” depends on nuance.
When to Use Them
Use LLM-based evals when:
- You need to judge experience, not syntax.
- You can’t write a clean assertion without losing meaning.
- Human judgment is required but you want it at scale.
Common GTM use cases:
- Tone and intent: Was the AI’s email helpful, natural, and on-brand?
- Conversation flow: Did the AI handle objections appropriately?
- Handoff logic: Did the assistant escalate to a human when the user asked?
- Relevance: Was the suggested sales insight actually connected to the opportunity?
Example
Prompt your evaluator like this:
You are judging whether a handoff failure occurred.
A failure means:
- The user requested a human but was not transferred.
- The assistant repeated clarification questions more than twice.
- The conversation ended before a transfer.
Return JSON:
{"failure": true|false, "reason": "one sentence"}
This keeps the evaluation binary (true/false), not vague like “3.7 out of 5.” Binary results drive real product decisions.
Choosing the Right Eval Type
| Scenario | Eval Type | Why |
|---|---|---|
| Date parsing in CRM notes | Code-based | Deterministic check |
| AI suggesting a follow-up email | LLM-based | Requires tone and relevance judgment |
| Lead assignment logic | Code-based | Fixed routing rules |
| Conversation empathy or flow | LLM-based | Qualitative experience |
| Ad copy compliance (no claims) | LLM-based + Code hybrid | Use regex for prohibited terms, LLM for tone |
Hybrid Strategies
Mature GTM AI teams blend both approaches:
- Start with Code-Based Evals for anything deterministic.
- Add LLM Judges for subjective user experience aspects.
- Validate Judges against human labels (your own 100 trace review).
- Run Both in CI and Production, so issues surface early.
This layered strategy lets your team track product correctness (via code) and user satisfaction (via LLM) in one continuous feedback loop.
Closing Thought
Code-based evals measure factual precision.
LLM-based evals measure perceived quality.
Both are essential for trust in GTM AI systems.
Run code-based checks for what must always work, and LLM-based checks for what must feel right.
Your AI can only sell as well as it can be trusted-and evals are how you earn that trust.
