Learn how to build a scalable backend for large language model (LLM) evaluation with a focus on testing, metrics, and compliance.
César Miguelañez
Feb 7, 2026
As large language models (LLMs) become central to AI-powered products, ensuring their quality, reliability, and alignment with intended behaviors is paramount. While many organizations rush to deploy generative AI (GenAI) applications, gaps in testing and evaluation often lead to reputational risks, operational failures, and compliance challenges. This article delves into the critical aspects of building a flexible backend for LLM evaluations, offering insights to both product managers and technical practitioners working with AI in production.
Why LLM Evaluation is Critical
In today’s fast-paced AI development landscape, releasing features is only half the battle. Ensuring these features work as intended and meet stakeholder expectations is a much bigger challenge. The importance of evaluation and quality assurance cannot be overstated, with industry leaders repeatedly emphasizing that evaluation accounts for up to 80% of the effort in AI product development.
The Risks of Inadequate Evaluations
Failing to rigorously test and validate LLM applications can expose organizations to various risks, including:
Reputational Damage: Errors or biases in AI outputs can harm a brand’s credibility.
Operational Failures: Business processes reliant on AI can break down unexpectedly.
Security Issues: Adversarial inputs or vulnerabilities in GenAI systems can lead to breaches.
Compliance Challenges: Regulations like the EU AI Act require adherence to strict guidelines, increasing the need for reliable evaluations.
The Gap Between Model Development and Application Testing
A key distinction highlighted is the difference between model evaluation (general-purpose performance testing) and application evaluation (specific use-case testing). While model evaluation focuses on how well an LLM performs across diverse scenarios, application evaluation ensures the model behaves as expected for a particular product or workflow. This divide underscores why testing and evaluation often prove more complex than implementation itself.
Categories of LLM Evaluations
To design a robust and dynamic evaluation backend, it’s important to understand the different approaches to evaluating LLMs. Evaluations can be broadly categorized into benchmark-based and judgment-based methods.
Benchmark-Based Evaluation
This method relies on predefined metrics and benchmarks to validate model performance. Examples include:
Multiple-Choice Evaluation: Models answer specific questions, and correctness is easily measured.
Verifier-Based Evaluation: Used for tasks like solving equations or generating code, where outputs can be verified against known answers.
Judgment-Based Evaluation
In this more subjective approach, performance is assessed based on human or LLM judges. Examples include:
Leaderboard Comparisons: Multiple LLMs respond to prompts, and evaluators rank their performance.
Custom Output Scoring: Using another LLM to assess the quality of generated results.
For practical purposes, single-turn testing (examining individual interactions) is often emphasized over multi-turn testing (evaluating entire conversations), as the latter is significantly more complex.
Building the Evaluation Backend: A Technical Perspective
A successful evaluation backend must balance flexibility and usability, catering to both highly technical developers and non-technical stakeholders such as product managers.
Key Design Principles for the Backend
Unified Interface for Metrics
The backend should integrate various evaluation libraries and metrics into a single interface. Developers used an adapter-wrapper pattern to harmonize disparate tools, ensuring seamless functionality. This allows users to leverage multiple libraries while maintaining simplicity.
Abstraction from Specific LLM Providers
To avoid vendor lock-in, the system should remain agnostic to specific LLM providers. By implementing abstract classes and factory patterns, developers can easily switch between providers like
OpenAI, Cohere, or Anthropic without overhauling the codebase.
Structured Outputs
Using structured formats like JSON for LLM outputs ensures consistency and eliminates unnecessary parsing. While prompting an LLM for structured data can sometimes degrade output quality, tools like
Pydantic schemas allow developers to define expected output structures, improving reliability.
Evaluation Metrics and Customization
Off-the-shelf metrics provided by libraries such as DeepEval or Tragas are a good starting point. However, many organizations require custom metrics tailored to specific applications. Customization enables users to define:
Evaluation Prompts: Input scenarios for testing.
Evaluation Steps: Criteria for assessing outputs.
Reasoning Frameworks: Clear justifications for scoring.
This iterative process ensures metrics align closely with the product’s goals.
Lessons Learned in Building AI Evaluation Systems
The presentation shares valuable insights for teams embarking on LLM evaluation projects. Here are the key lessons:
Focus on Architecture First
Before designing prompts or workflows, establish a solid architectural foundation. This ensures scalability and adaptability as the system grows.
Measure What Matters
Prioritize metrics that directly impact your application’s use case. Avoid being distracted by metrics that don’t serve your specific needs.
Stay Model-Agnostic
Flexibility is key. Build systems that allow for easy integration of new LLM providers or evaluation tools.
Avoid Over-Reliance on AI Assistance
While using LLMs for coding or evaluations can accelerate development, over-reliance can create technical debt. Ensure your codebase remains clean, understandable, and extensible.
Key Takeaways
Evaluation is Critical: Testing and quality assurance account for the majority of effort in AI development, ensuring applications are reliable and compliant.
Distinguish Between Evaluation Types: Understand the differences between model evaluation (general-purpose) and application evaluation (use-case specific).
Leverage Unified Interfaces: Use adapter patterns to integrate multiple evaluation libraries seamlessly.
Avoid Vendor Lock-In: Keep your system flexible by abstracting dependencies on specific LLM providers.
Embrace Structured Outputs: Use tools like Pydantic schemas to define and enforce output formats for LLMs.
Develop Custom Metrics: Tailor evaluation criteria to the unique goals of your application.
Think Architecture First: A well-designed backend ensures scalability and reduces long-term technical debt.
Iterate and Improve: Continuously refine your evaluation system based on real-world usage and feedback.
Conclusion
Building a robust and flexible LLM evaluation backend is essential for teams deploying AI-powered products. By focusing on architecture, adopting flexible design patterns, and tailoring evaluation metrics, organizations can ensure their applications meet the highest standards of quality and reliability. As the AI landscape evolves, proactive evaluation strategies will remain the bedrock of successful product development.
This approach not only mitigates risks but also fosters collaboration between product managers and technical teams, ensuring that AI solutions align with both business goals and user expectations. Prioritize evaluation today to build the resilient AI systems of tomorrow.
Source: "Designing a Flexible Evaluation Backend for LLM Applications" - Global AI Community, YouTube, Jan 7, 2026 - https://www.youtube.com/watch?v=ZODp9UF3R8c
