This content originally appeared on DEV Community and was authored by Muhammad Mairaj
What are evals?
Evals, short for evaluations, are systematic processes designed to measure and benchmark the performance of AI models, prompts, and workflows. In the context of AI and machine learning, evals refer to structured methods of testing whether a system produces outputs that meet predefined quality, accuracy, or safety standards.
Key terms to understand here:
- Model evaluation: Testing how well a model performs on tasks like summarization, classification, or reasoning.
- Prompt evaluation: Measuring the consistency, accuracy, and reliability of a prompt across variations.
- Benchmarking: Comparing one system against others using shared datasets or criteria.
Put simply, evals are the feedback loops that ensure AI systems are reliable, safe, and useful.
Why evals matter in 2025
With the rise of AI-powered applications across industries, evals have become critical to trust and adoption.
According to Stanford’s 2024 AI Index, 52% of companies report challenges in measuring the reliability of generative AI outputs, and nearly 68% of executives say they are investing in evaluation frameworks to reduce hallucinations and compliance risks.
As Andrew Ng put it: “AI systems are only as good as the evaluations we run on them. Without proper testing, you’re essentially flying blind.”
Key reasons evals matter today:
- Reliability: Users need consistent answers from AI systems. Safety: Evals catch harmful or biased outputs before deployment. Regulation: Governments are introducing audit requirements (e.g., EU AI Act).
- Trust: Businesses and end-users rely on transparent evaluation scores. Iteration: Continuous evals help refine prompts, models, and workflows.
In short, evals are no longer optional - they are a competitive and compliance necessity.
How to implement evals in your AI workflows
Implementing evals requires a structured approach that balances automation with human review. Here’s a step-by-step framework:
Step 1: Define evaluation goals
What do you want to measure - accuracy, safety, style, or factual correctness? Choose metrics aligned with your business use case.
Step 2: Select test datasets
Use real-world samples from your domain (customer queries, documents).
Consider synthetic datasets for edge cases.
Step 3: Choose evaluation methods
Automated metrics: BLEU, ROUGE, accuracy, latency.
LLM-as-a-judge: Using another model to grade outputs.
Human review: Domain experts validating results
Step 4: Run iterative evaluations
Automate periodic evals during development.
Track performance trends over time.
Step 5: Integrate with deployment pipeline
Add evals as CI/CD checks before shipping changes.
Automate alerts if scores drop below thresholds.
Next step prompt: Once you’ve completed basic evals, ask: How do I expand my evaluation system to cover fairness, safety, and compliance?
Evals vs alternatives
While evals are central, they are often compared against other quality-assurance approaches like A/B testing, user surveys, or synthetic monitoring.
Feature comparison table
Pros and cons of evals
Pros
- Objective and repeatable
- Scalable across tasks and models
- Detects hidden weaknesses
Cons
- Requires dataset preparation
- May not capture “real-world” nuance without human input
- Setup can be resource-intensive initially
Use case scenarios
- Enterprise AI apps: Ensure compliance with legal standards.
- Consumer chatbots: Test reliability before mass rollout.
- Healthcare AI: Verify factual correctness and reduce bias.
Frequently asked questions
Q1: How are evals different from benchmarks?
A: Benchmarks are shared, static test sets, while evals can be customized for your use case. Evals are ongoing, whereas benchmarks are more like snapshots.
Q2: Do I always need humans in the loop for evals?
A: Not always. Automated metrics and LLM-as-a-judge methods are effective for many tasks, but human validation is critical in regulated or high-stakes domains.
Q3: Can evals prevent hallucinations?
A: They don’t eliminate hallucinations entirely, but they can detect and reduce them significantly when paired with guardrails.
Q4: How often should I run evals?
A: Best practice is to run them continuously - at least before every major deployment or prompt update.
This content originally appeared on DEV Community and was authored by Muhammad Mairaj