Building Reliable AI Applications: Why Evals (and Scenarios) Are the backbone of trustworthy AI
Manouk Draisma
Sep 25, 2025
When it comes to building reliable AI applications, one discipline stands above the rest: LLM evaluations (evals).
In 2023, most AI product teams hadn’t even heard the term. Today, leading companies like OpenAI, Anthropic, and Google DeepMind openly say that evals are the most critical skill for AI product managers and engineers. They’re not hype—they’re the backbone of trustworthy AI.
But here’s the problem: many teams still don’t know how to use evals effectively. Some try them once, fail, and conclude they don’t work. Others reduce evals to “unit tests with LLMs” and miss the bigger picture.
This guide explains what evals really are, why they’re essential to LLMOps evaluations, AI testing, and AI agent testing, and how to implement them for maximum ROI. We’ll also introduce Scenarios—a new way to make evals actionable by grounding them in real-world contexts.
What are Evals in AI?
An eval is a systematic method to measure and improve an AI application.
Think of evals as analytics for AI outputs. Without them, you’re flying blind—relying on “vibe checks” where you test a few prompts by hand and hope you didn’t break something else in the system. That works for prototypes, but it fails the moment you scale.
Evals create a feedback loop. They tell you:
Where your model fails
Which improvements matter most
Whether new changes improve or regress performance
This is why evals are often called the new unit tests for AI. But unlike classic software tests, evals capture the gray areas: ambiguity, user experience, and product alignment.
They are also the foundation of LLMOps evaluations, ensuring that your AI lifecycle includes continuous LLM monitoring and AI observability.
Why AI Product teams need Evals
Without evals, teams fall into two traps:
Unclear quality – you don’t know if your latest model change is better or worse.
Hidden regressions – small issues slip through and pile up, eroding trust with users.
With evals, you gain:
Confidence in releases – know before shipping whether you’ve improved or broken something.
Continuous learning – error patterns surface in production and feed into product iteration.
Alignment with business goals – evals measure success not just technically, but in terms of customer outcomes.
For AI-first startups and enterprise teams alike, evals are the missing bridge between LLMOps experimentation, AI testing, and AI product quality assurance.
Example: Evals in a Real Estate AI Assistant
Picture you’re building an AI assistant for a real estate company.
A lead texts:
“Do you have a one-bedroom with a study available?”
The assistant replies:
“We have one-bedrooms, but none with a study.”
Technically, this is correct.
But as a sales assistant, it fails—the conversation ends instead of retaining the lead.
An eval would flag this as a lead retention failure. Without evals, you’d only catch this in production after lost deals.
This is why evals go beyond factual correctness. They ensure the AI aligns with your product’s success metrics, not just raw truth.
Introducing Scenarios: Beyond Isolated Evals
Evals give you the what: pass/fail signals on model outputs. But real users don’t interact with AI in single prompts—they interact across multi-turn flows, business processes, and edge cases.
That’s where Scenarios come in.
A Scenario is a structured simulation of a real-world interaction or workflow. Instead of just checking one output, scenarios test how an AI system behaves across a sequence of inputs, states, and goals.
Where evals are the unit tests, scenarios are the integration tests of AI. They let you capture business-critical behaviors like:
Customer support resolution (did the user’s problem actually get solved?)
Compliance workflows (did the AI follow required handoff rules?)
Sales conversions (did the conversation move toward closing, not just answering?)
This is why LangWatch built Scenarios — bridging the gap between evals and real-world AI agent testing.
Industry-Specific Use Cases for Evals & Scenarios
Fintech & Banking
Agent to approve payments —> catch issues before they arize
Run scenarios for Agentic chatbots to prevent brand reputation or incorrect information
Health Tech
Test patient-facing chatbots for empathy and accuracy.
Ensure adherence to HIPAA/GDPR in sensitive data handling.
Customer Support (US & Global)
Run multi-turn scenarios that validate resolution, tone, and escalation.
Monitor regressions in ticket classification and response quality.
E-commerce & Retail
Test AI product recommenders for diversity, personalization, and upselling.
Ensure smooth AI agent testing for returns, refunds, and substitutions.
The evaluation process: from chaos to signal
So how do you implement LLM evals in practice? Here’s a proven framework:
Error analysis (open coding)
Start with real production traces. Sample 100–200 interactions. Write down what feels wrong.“Should have escalated to a human agent.”
“Hallucinated a feature we don’t have.”
Categorize errors (axial coding)
Cluster raw notes into categories:Handoff failures
Hallucinations
JSON/output formatting errors
Broken conversational flow
Prioritize failure modes
Not all errors matter equally. Use frequency × impact to pick the top 3–5 recurring issues.Build Evaluators
For each failure mode, create evaluators:Code-based evaluators (e.g., JSON schema validation).
LLM-as-judge evaluators (e.g., “Did the assistant handle escalation appropriately?”).
Always make them binary (pass/fail). Avoid vague Likert scales.
Validate Your Judges
Compare LLM-as-judge results against manual annotations. Iterate until alignment is high.
How Many Evals Do You Need?
Surprisingly, most teams find they only need 4–7 evaluators.
Why? Because:
Many problems can be solved with better prompts or guardrails.
Evaluators should focus on recurring, high-impact failure modes.
The goal isn’t to eval everything—it’s to build a small, high-signal suite that reflects your product’s success metrics.
Scenarios then layer on top, combining these evaluators into holistic tests that reflect your product in the real world.
Common Misconceptions About AI Evals
“LLMs can eval themselves.”
Not reliably. Without context, models often give false positives. Human validation is essential.“Evals slow us down.”
The opposite. They prevent regressions, eliminate guesswork, and speed up iteration.“Evals are just AI unit tests.”
They’re broader—covering error discovery, production monitoring, and product-aligned success criteria.
Why Some Teams Say “Evals Don’t Work”
The controversy around evals usually comes from poor implementation:
Using vague rating scales instead of binary checks
Running evals on artificial test sets instead of real traces
Blindly trusting LLM-as-judge without validation
Top labs may claim they rely on “vibes,” but under the hood, they all have eval-heavy pipelines. Error analysis and evals drive their iteration speed.
The truth: evals are just data science applied to AI applications. They’re not magic—just structured analytics adapted to generative AI.
Evals as the New PRDs
A fascinating shift: many AI teams now treat evals as their living product requirements documents (PRDs).
A traditional PRD defines expected behavior.
An eval encodes that behavior in a testable way.
The difference: PRDs get forgotten in Confluence. Evals run continuously, ensuring your AI behaves as intended.
Scenarios extend this further—they don’t just test isolated expectations, but full workflows and success criteria. They become the living proof that your AI product aligns with real business outcomes.
Final Thoughts: Why Evals (and Scenarios) Are High-ROI
Evals are not optional. They’re the highest ROI activity in AI product development because they:
Turn vague expectations into measurable signals
Catch product-breaking issues before users do
Align AI behavior with business outcomes
Build confidence in every release
And with scenarios, you can go beyond unit-level correctness to ensure real-world reliability.
As the AI ecosystem matures, evals are becoming as fundamental as CI/CD in software engineering. The teams that master both evals and scenarios will ship faster, fail less, and win customer trust.
FAQs (SEO Schema-Ready)
Q1: What are LLM evaluations in AI?
LLM evaluations (evals) are systematic tests that measure and improve AI model performance, ensuring alignment with business outcomes.
Q2: How do scenarios differ from evals?
Evals test single outputs; scenarios simulate end-to-end workflows for AI agent testing and business-critical success.
Q3: Why do teams say evals don’t work?
Usually from poor implementation—using vague rating scales, fake test sets, or unvalidated LLM-as-judge.
Q4: How many evals does a team need?
Most teams succeed with 4–7 evaluators, layered into broader scenarios for complete covera
