Sep 15, 2025
Building AI-powered applications, especially those incorporating large language models (LLMs), is no small feat. While the rapid pace of AI advancements has inspired many businesses and startups to explore possibilities, the reality is that most generative AI projects fail or face significant challenges. In fact, reports suggest that by 2027, 40% of agentic AI projects may be canceled, and 95% of generative AI pilots are currently failing to meet their goals. Success requires more than just deploying a system - it demands a systematic approach to evaluation and improvement.
This comprehensive guide explores the intricacies of LLM evaluations, offering actionable strategies to help engineers, businesses, and startups reliably ship AI applications and continuously optimize them for performance and results.
Why Are LLM Evaluations So Crucial?
LLMs are powerful but unpredictable. Their outputs are nondeterministic, sensitive to context, and highly variable in quality. For example, two users might ask the same question but receive different answers based on slight differences in phrasing. This variability creates challenges for businesses relying on AI to deliver consistent, reliable user experiences.
LLM evaluations help solve these issues by:
Measuring performance: Identifying areas where the system performs well - and where it struggles.
Preempting failures: Catching issues before they impact users or business outcomes.
Driving continuous improvement: Iteratively optimizing the system with real-world data and feedback.
Understanding the Core Challenges in LLM Development
When building AI systems with LLMs, there are three primary challenges:
1. Understanding the Data
At scale, user interactions become unpredictable.
It’s easy to design workflows for "happy paths" (ideal scenarios) but far harder to account for edge cases and unexpected inputs.
2. Bridging Specification Gaps
Translating what you want the AI to do into precise prompts, instructions, or workflows is harder than it seems.
Even when outputs "look" good, the underlying issues (e.g., whether the system misinterprets certain queries) may remain hidden.
3. Ensuring Consistency
Small changes in prompts, models, or contexts can drastically alter outputs.
Without proper evaluations, systems risk behaving inconsistently, leading to potential loss of trust from users.
Overcoming these challenges requires a framework for evaluating, diagnosing, and improving AI systems systematically.
The Three Levels of LLM Evaluations
LLM evaluations can be categorized into three levels, each with increasing complexity and cost:
Level 1: Unit Tests
Unit tests are fast, automated assertions designed to check the core functionality of your system. These are comparable to traditional software tests, ensuring that specific tasks are executed correctly.
Example Use Case:
If your AI categorizes a customer query ("My credit card was charged twice") as Billing, a unit test would ensure that this categorization is accurate.
Implementation Tips:
Use Python’s
assertstatements to validate outputs.Focus on structured outputs like categories, confidence scores, or response formats.
Collect raw events and examples to continually expand your test cases.
When to Use: Every time you make code or prompt changes. These are cheap, fast, and should be your first line of defense.
Level 2: Human and Model Evaluations
This involves a systematic review of outputs by humans (domain experts) or automated critiques by LLMs. Human oversight is essential for creating ground truth evaluations, especially in the early stages.
Key Considerations:
Human evaluations should focus on real-world examples and involve domain experts to assess whether outputs align with business goals.
Model evaluations (using LLMs as judges) can critique outputs based on predefined criteria, such as accuracy, tone, and helpfulness.
Steps to Implement:
Collect input-output pairs from your system.
Have humans evaluate these pairs to establish a benchmark.
Train an LLM evaluator aligned with human judgments (using feedback and iterative prompt refinement).
Track agreement between human and model evaluations.
Pro Tip: Start with binary ratings (e.g., "Good" or "Bad") before moving on to more complex scoring systems.
When to Use: Weekly or bi-weekly reviews of production systems, especially when introducing new features.
Level 3: A/B Testing
A/B testing involves real-world experiments to compare different versions of your AI system. It’s especially useful for evaluating major changes, such as switching models, adjusting prompts, or introducing new workflows.
Metrics to Measure:
User satisfaction: Surveys, feedback ratings, or thumbs-up/down responses.
Task completion rate: How effectively users accomplish their goals.
Business outcomes: Sales, retention, or engagement metrics.
Challenges:
A/B testing requires significant data (and users) to produce statistically valid results.
It is more resource-intensive and typically reserved for mature systems with established user bases.
When to Use: For major releases or to measure the impact of high-level business outcomes.
Building an Evaluation-Driven Workflow
Evaluations shouldn’t be an afterthought - they should be integrated into every step of your AI development process. A systematic evaluation cycle can help you improve faster and more reliably:
Analyze: Identify failure modes by reviewing data, errors, and user feedback.
Measure: Translate insights into quantitative metrics, starting with simple ones (e.g., Pass/Fail).
Improve: Refine prompts, models, or workflows based on evaluation data.
This cycle ensures that your AI system evolves with real-world usage, not just laboratory conditions.
Common Mistakes to Avoid
1. Over-reliance on Tools
Don’t jump to buying fancy evaluation platforms before understanding your data and challenges. Start simple with custom solutions tailored to your needs.
2. Generic Metrics
Avoid drowning in meaningless scores (e.g., "Helpfulness: 4.2"). Instead, focus on actionable, domain-specific metrics.
3. Skipping Human Evaluation
Automated tools and LLM evaluators are only as good as the human insights they’re based on. Always start with human critiques to establish a baseline of quality.
4. Neglecting Data
Constantly inspect real user data and raw events. Engineers who avoid this critical step risk losing valuable insights into their system’s behavior.
Key Takeaways
LLM evaluations are essential for creating reliable, scalable, and user-friendly AI systems.
Start with simple unit tests to catch basic errors early.
Incorporate human evaluations to establish quality benchmarks before automating with LLM evaluators.
Iterate using the Analyze-Measure-Improve cycle to refine your system continuously.
Use A/B testing sparingly, focusing on significant changes and mature systems.
Always look at real-world data, and don’t rely solely on automated metrics.
Avoid generic tools and solutions - tailor metrics and evaluations to your specific business needs.
Conclusion
In the competitive world of AI app development, systematic evaluations are non-negotiable. Businesses, startups, and entrepreneurs can’t afford to rely on guesswork or superficial metrics. By adopting a robust evaluation-driven workflow, you can ensure that your AI systems not only meet user expectations but also evolve to deliver long-term value.
Whether you’re just starting with AI or iterating on a mature platform, the principles outlined here can help you build better systems, faster - turning challenges into opportunities for innovation and growth.
Source: "LLM Evaluations Crash Course for AI Engineers" - Dave Ebbelaar, YouTube, Jan 1, 1970 - https://www.youtube.com/watch?v=a3SMraZWNNs
Use: Embedded for reference. Brief quotes used for commentary/review.
