How Evals Engineering Catches GenAI Failures Before Users Do

Your LLM passed every benchmark, then hallucinated pricing information to a customer. Your RAG pipeline tested fine in staging, then pulled irrelevant context in production. You found out when support tickets started piling up.

Traditional ML evaluation doesn't catch these failures. Accuracy and precision work for classifiers. GenAI systems need different measures, hallucination rates, context adherence, response quality, and toxicity. Most teams don't have systematic ways to track any of it.

Evals engineering closes this gap. This article explores the key components required to develop evaluation systems that identify failures before users do.

What is evals engineering?

Evals engineering is the discipline of designing, implementing, and managing evaluation processes for GenAI systems. The goal is straightforward: measure whether your LLMs, RAG pipelines, and fine-tuned models actually work, and keep working as they scale.

Traditional software testing checks if code does what it's supposed to. Evals engineering asks a harder question: Is the AI output good enough? "Good enough" means different things depending on context: accurate, safe, relevant, non-toxic, and on-brand. Defining and measuring these qualities is the core of the discipline.

The scope covers the full GenAI stack:

• LLMs: Are responses accurate, coherent, and free of hallucinations?

• RAG pipelines: Is the retrieved context relevant? Does the model use it correctly?

• Fine-tuned models: Does performance hold across edge cases and new inputs?

• Agents: Do tool selections and action sequences make sense? (This is its own specialization, agent evaluation engineering.)

Most teams treat evaluation as a gate, run some tests, check the boxes, and ship. That approach falls apart with GenAI. Models behave differently in production than in testing. User inputs are unpredictable. Quality degrades over time. You need evaluation running continuously, during development, before deployment, and in production.

Good evals give you actionable data, what's broken, where, and why. Get this right, and you catch quality issues before users do. Skip it, and you find out about problems when complaints start rolling in.

Why GenAI teams need evals engineering

GenAI systems fail in ways traditional software never did. A classifier gives you a wrong label, you retrain it. An LLM gives you a confident, well-written response that's completely made up. Users can't tell the difference. Neither can your support team until the complaints stack up.

Manual review worked when you had a handful of outputs to check. Now you're processing thousands of LLM responses daily. Nobody has time to read them all. And even if they did, human reviewers miss things. They get tired. They apply criteria inconsistently.

Here's what happens without systematic evaluation:

• Hallucinations reach customers: The model invents facts, and nobody catches it.

• Quality drifts silently: Performance degrades over weeks, and you don't notice until it's bad.

• Edge cases slip through: Rare inputs trigger failures you never tested for.

• Debugging becomes guesswork: Something broke, but you can't pinpoint where or why.

The teams that scale GenAI successfully build evaluation into their infrastructure. They catch issues early, track quality over time, and fix problems systematically. Everyone else plays whack-a-mole with production fires.

Evals engineering vs traditional ML evaluation

Traditional ML evaluation assumes you know what "correct" looks like. You have labeled data. The model predicts, you compare, you get a score. Clean and repeatable.

GenAI breaks this model. When an LLM generates a paragraph, there's no single correct answer to compare against. Two completely different responses can both be good—or both be subtly wrong in ways that labels can't capture.

The metrics change too. Accuracy doesn't tell you if a response hallucinates facts. Precision doesn't measure whether the tone was appropriate. You need new measures, hallucination rate, context adherence, toxicity scores, brand alignment—that traditional ML evaluation never considered.

Scale compounds the problem. Traditional test sets have thousands of examples. GenAI systems generate millions of outputs. You can't manually review them. You need automated evaluation that runs continuously, flags issues in real-time, and surfaces patterns across massive volumes.

What are the types of evaluation in GenAI systems?

Evaluation happens at two stages: before deployment and after. Most teams focus on the first and ignore the second. That's a mistake. GenAI systems behave differently in production than in testing. You need coverage at both.

Pre-deployment evaluation

Before anything goes live, you test against controlled scenarios. Benchmark datasets, synthetic inputs, and known edge cases. The goal is catching obvious problems early, hallucinations on common queries, toxic outputs, and failures on predictable inputs.

Prompt evaluation matters here. Small changes in prompts produce wildly different outputs. You need systematic ways to test prompt variations before they hit production. Does the model handle rephrased questions? Does it break when users add irrelevant context? These patterns show up in pre-deployment testing if you look for them.

Pre-deployment evals give you a baseline. They tell you the model is ready enough to ship. They don't tell you how it will behave with real users.

Production evaluation

Production is where GenAI actually gets tested. Real users, real queries, real edge cases you never thought to simulate. This stage requires continuous monitoring—evaluation that runs on live traffic, flags anomalies, and tracks quality over time.

The questions change. You stop asking "did it pass?" and start asking "how is it performing?" Quality that looked fine at launch can degrade over weeks. User inputs drift. Model behavior shifts. You need visibility into what's happening now.

The best eval teams build feedback loops between production and development. Production failures become new test cases. Quality issues get traced to root causes. The evaluation system learns from real-world behavior.

What are the key metrics for evals engineering?

GenAI outputs don't come with answer keys. You can't compare a generated paragraph against a labeled dataset and calculate accuracy. The metrics that matter here measure different qualities, whether responses are grounded, complete, safe, and actually useful.

• Context adherence: Measures whether the model's response is grounded in the provided context. Essential for RAG systems, did the model actually use what it retrieved?

• Correctness: Tracks whether the facts stated in the response are based on real facts. A model can sound confident while making things up entirely.

• Uncertainty: Measures the model's certainty in its generated responses. High uncertainty correlates strongly with hallucinations and made-up facts.

• Completeness: Evaluates how thoroughly the response covered relevant information from the context. If context adherence is your RAG precision, completeness is your recall.

• Instruction adherence: Measures whether the model followed its instructions. Critical when you have specific formatting, tone, or content requirements.

• Chunk attribution: Measures which chunks retrieved in a RAG workflow actually influenced the response. Helps you rightsize retrieval and avoid paying for unused context.

• Toxicity: Catches abusive, toxic, or harmful language before it reaches users.

• PII detection: Surfaces any credit card numbers, social security numbers, phone numbers, or email addresses in model responses.

• Prompt injection: Identifies adversarial attacks or attempts to manipulate model behavior.

• Latency: Tracks how long users wait. Slow responses frustrate users and timeout mid-workflow.

What are the best practices for evals engineering?

Building evals infrastructure takes time. Most teams start with manual spot-checks, realize that doesn't scale, then scramble to build something systematic. The teams that get this right follow a few core principles.

Start with eval-driven development

Build evals into your development workflow from the start. Every prompt change, every model update, every retrieval tweak should trigger an evaluation run that blocks releases failing your quality thresholds.

Define what "good enough" looks like before you ship. Context adherence above 90%? Hallucination rate below 5%? Latency under 2 seconds? Write these thresholds down and treat them as non-negotiable gates in your release process.

The reasoning is straightforward: finding a hallucination in development costs you a quick prompt fix. Finding it in production costs you an angry customer, a support ticket, and engineers pulled off their actual work. Teams that skip this step end up playing catch-up—shipping something that demos well, then spending weeks debugging issues that pre-deployment evals would have caught in minutes.

Most teams try to build this into their CI/CD manually. The faster path is purpose-built tooling. Galileo Experiments runs systematic evaluations against versioned datasets, bringing CI/CD rigor to AI workflows without custom scripting.

Automate scoring wherever possible

Manual review doesn't scale. When you're processing thousands of LLM responses daily, spot-checking a handful tells you nothing about overall quality. 

Human reviewers also introduce inconsistency, one flags a response as problematic while another lets it pass. Over time, fatigue sets in and standards drift. Automated evaluation applies the same criteria to every output, every time.

The challenge is cost. LLM-based evaluation is powerful but expensive at scale. Using GPT-4 to evaluate every output adds up fast. Many teams start with comprehensive automated evals, then scale back when the bills arrive. That's the wrong trade-off—you end up with coverage gaps exactly where you need visibility most.

The solution is smaller, purpose-built evaluation models. They run faster, cost less, and can assess every output instead of random samples. A well-tuned small model catches the same issues as a large one at a fraction of the cost.

Galileo's Luna-2 models do exactly this, assessing outputs across dozens of quality dimensions at 97% lower cost than traditional LLM-based approaches.

Monitor continuously in production

Pre-deployment evals tell you the model is ready to ship. Production monitoring tells you how it's actually performing. Different questions, different answers.

Quality drifts over time as user inputs change in ways you didn't anticipate. A system that passed all checks can degrade over weeks—you won't notice until complaints stack up. Production also surfaces edge cases no test suite can anticipate. Real users ask questions your synthetic data never did and find gaps faster than any QA process.

Track key metrics on live traffic. Set alerts for quality drops. Surface anomalies before they cascade. Segment monitoring by input type, user cohort, and use case—aggregate metrics hide problems affecting specific queries or user groups.

Building this monitoring layer from scratch takes months. Galileo provides real-time visibility out of the box, tracking live traffic, flagging anomalies, and surfacing quality trends before they reach customers.

Build feedback loops

Production failures should feed back into development. Every incident becomes a new test case. Every edge case gets added to your eval suite. The system learns from real-world behavior rather than hypothetical scenarios.

When you find a new failure mode in production, add it to your test suite. When a prompt change fixes an issue, capture that scenario so you don't regress later. Your evals get smarter over time because they're built on actual failures.

This also preserves institutional knowledge. When an engineer debugs a tricky issue, that learning typically lives in their head or a Slack thread. Turning it into an evaluation case means new team members inherit those lessons. The test suite becomes a record of every hard problem the team has solved.

Doing this manually is tedious and easy to skip under deadline pressure. Galileo's Insights Engine automates the process, clustering similar failures, surfacing root-cause patterns, and recommending fixes so your eval system improves without extra effort.

Ship reliable GenAI systems with Galileo

You've seen what evals engineering requires—systematic testing in development, automated scoring at scale, real-time production monitoring, and feedback loops that learn from failures. Building this infrastructure from scratch takes months. Most teams don't have that runway.

Galileo provides the complete evaluation and observability stack:

• Automated quality guardrails in CI/CD: Galileo integrates directly into your development workflow, running evaluations on every change and blocking releases that fail quality thresholds.

• Low-cost automated scoring with Luna-2: Assess every output across dozens of quality dimensions, correctness, context adherence, toxicity, instruction following—at 97% lower cost than traditional LLM-based evaluation.

• Real-time production monitoring: Track live traffic, flag anomalies, and surface quality trends before they become customer complaints.

• Intelligent failure detection: Galileo's Insights Engine clusters similar failures, surfaces root-cause patterns, and recommends fixes—building institutional knowledge as you go.

• Built-in guardrail metrics: Context adherence, hallucination detection, PII scanning, prompt injection detection, and more, ready to use out of the box.

Get started with Galileo and build GenAI systems your users can trust.