Let’s get straight to the point—this post is vital and couldn’t have come at a better time.
As QA professionals, we’ve always been the gatekeepers of software quality. But with the rise of AI and LLMs, our role is evolving.
Writing evaluations—assessments of AI systems—is quickly becoming a core skill for anyone working with AI products, and soon, this will include nearly everyone in tech. However, there’s still very little guidance on how to evaluate these systems effectively.
In this post, we’ll dive deep into what evaluations are, why they’re critical for ensuring AI quality, and how you, as a QA expert, can master this essential skill to stay ahead in the rapidly evolving landscape of AI-powered applications.
After all, evaluations quietly decide whether your product thrives or dies.
The Buzz Around Evals
If you’ve been following the AI space, you’ve likely heard the buzz around evals. Industry leaders agree that evaluating LLMs is key to building effective AI products. As AI continues to grow, learning how to run evals will become a must-have skill for QA professionals, product managers, and developers.
In this blog, we’ll explore the significance of evals, how they differ from traditional software testing, and why they are vital for maintaining the reliability of LLM applications. We’ll also answer some critical questions:
- What exactly are evals?
- How do they differ from traditional software testing methods?
- Why are they essential for ensuring the quality of LLM-based applications?
- How can QA professionals play a role in LLM evaluation?
As the world moves closer to fully integrating AI in everyday applications, understanding and mastering evals will empower QA professionals to lead in the evaluation of LLM-powered products, ensuring their quality and reliability.
Software Testing vs LLM Evals
While both software testing and LLM evaluation aim to ensure product quality, there are distinct differences between the two. Here’s a quick comparison:
What Exactly Are Evals?
Evals, short for evaluations, are methods used to assess the quality and performance of AI systems, particularly those powered by LLMs. Instead of the traditional, deterministic approach to software testing, evals measure the more subjective qualities of AI outputs, such as:
- Relevance: Does the AI output align with the user’s intent?
- Coherence: Does the output make sense in context?
- Safety: Does the AI generate harmful, biased, or inappropriate content?
Think of evals as the new standard for testing AI, just like regression or load tests in traditional software. However, since LLMs can give different results for the same input, they need more thoughtful, flexible evaluation methods.
Why Evals Matter for LLM-Based Applications
When it comes to LLMs, evals aren’t optional—they’re essential.
AI-powered tools like chatbots, product recommenders, or customer support agents need to give answers that are accurate, relevant, and most importantly, trustworthy. If they mess up, it doesn’t just confuse users—it damages trust and your brand.
A Real Life Example
Let’s say you're building an AI shopping assistant.
A user asks:
“Can you help me find a reliable laptop under $800?”
You pick a powerful language model—GPT-4.1, Claude, or Gemini—and craft prompts to help it understand the query.
At first, it performs well—simple questions, simple answers.
Then you integrate real-time tools like:
- A product database
- Price comparison APIs
- Review scoring
Now your assistant can recommend products based on real data. It looks ready for launch.
Then It Breaks
Once it goes live, users flood support with complaints.
“Why is it recommending $1,500 gaming laptops? I asked for budget ones!”
The assistant misunderstood the request. And the issue wasn’t caught during testing.
Why? Because traditional tests don’t dig deep enough into the real user experience.
This is Where Evals are Needed
Evals will help you:
- Catch misunderstood prompts early.
- Tests for hallucinations or biased responses.
- Measure how well the model handles complex, real-world tasks.
- Ensure that the AI stays helpful, safe, and on-brand.
Without proper evaluation, even the best models can lead users in the wrong direction.
Key Benefits of Evals
- Validation: Ensure LLMs avoid errors like hallucination (making up information) or producing inappropriate content.
- Unified Understanding: Help development and QA teams align on quality standards and improve communication across teams.
- Continuous Improvement: Identify areas for refinement in the model or prompt design, enabling iterative enhancement of the AI system.
- Impact Measurement: Track the effects of specific changes (e.g., updated prompts) on LLM performance to make informed decisions for future iterations.
Different Approaches to LLM Evaluation
There isn’t a one-size-fits-all approach to LLM evaluation. The most effective strategies combine several methods. Let’s explore some key approaches:
- Human Evals: Direct feedback from users or subject-matter experts helps assess the quality of generated outputs.
- Code-Based Evals: Automated checks that validate aspects like code correctness or API call reliability.
- LLM-Based Evals: Leveraging a second LLM to evaluate the output of the primary LLM, providing an efficient and scalable method for evaluation.
- Automatic Evaluation Metrics: Metrics like F1 score or BLEU score that automatically assess text generation quality.
Each of these methods plays a critical role in evaluating LLMs, and a combination of these approaches will provide the most comprehensive insights into system performance.
The Challenges of LLM Evaluation
Unlike traditional software testing, evaluating LLMs comes with unique challenges:
- Lack of Ground Truth: Many LLM tasks don’t have a single correct answer, requiring a nuanced approach to evaluation.
- Contextual Dependence: The quality of an LLM’s output can depend heavily on the specific context in which it is used.
- Bias and Ethical Concerns: LLMs can sometimes produce biased or unethical outputs, and evals must assess and mitigate these risks.
Despite these challenges, mastering LLM evaluation is essential for ensuring the safety and effectiveness of AI systems.
Conclusion
In the future, the LLM evaluation will be an essential piece of the QA territory as AI continues to grow. Following this guide, QA professionals can make sure that with all their trust, the AI-powered products meet the best quality, safety, and reliability.
At Alphabin, we specialize in LLM evaluations. Combining our QA expertise with modern AI testing practices, we help you design effective evals, automate workflows, and ensure your AI systems are accurate, safe, and reliable.
Want to ensure your LLM applications are reliable and high-quality? Contact Alphabin—our expert team will help you build trustworthy AI systems that meet the highest standards.
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