The Ultimate Guide to Generative AI App Evaluation

The Need for Robust LLM Application Evaluation

When companies approach us, they often express frustration with existing evaluation approaches, citing challenges that impact their ability to deliver consistent, high-quality results. Three statements we commonly hear reflect the gaps they experience:

• “Human ‘vibe-checks’ on quality aren’t going to get us to where we need to be.”
• “For customer growth & retention, it’s critical to be able to demonstrate that we achieve the highest bar for quality and accuracy.”
• “I don’t think fully automated evaluation will work well enough given how complex & domain-specific our application is.”

These concerns underscore the importance of a structured, reliable evaluation framework.

What is LLM Application Evaluation, and Why Does It Matter?

When we talk about evaluating LLM applications, we’re not discussing the evaluation of foundation models (like large-scale benchmarking, red-teaming, or AI safety assessments). Instead, we’re focused on the real-world performance of applications built on these foundation models.

Evaluating LLM applications is essential to ensure that they deliver reliable, high-quality results for end-users. Poor evaluation practices have led to significant real-world consequences, including financial losses, reputational damage, and even severe safety risks. Here are a few real-world examples of what can go wrong without rigorous evaluation:

• In customer service, chatbots have mistakenly offered substantial refunds or credits for travel bookings, leading airlines to face costly, unplanned refunds and loss of customer trust. In one case, a chatbot even incorrectly promised large refunds to numerous users, creating a costly mistake for the company and damaging its reputation.
• In automotive sales, an AI-powered sales assistant made an error that listed cars at drastically reduced prices—causing confusion, customer dissatisfaction, and significant financial impact on the dealership.
• In mental health, AI therapy applications have raised ethical concerns after reportedly responding in ways that exacerbated users’ mental health challenges, with some cases tragically linked to individuals’ decisions to harm themselves. These applications highlight the critical importance of careful evaluation and ethical guidelines in sensitive domains.
• In finance, LLM tools assessing loan applications or flagging fraud have incorrectly classified applicants, leading to biased lending decisions and, in some cases, missed fraud that resulted in substantial financial losses.

Without rigorous evaluation, LLM applications can misinterpret requests, make costly mistakes, or even provide unsafe advice, as these examples show. Proper evaluation ensures these applications function safely and consistently, meeting standards of quality, accuracy, and ethical responsibility. For businesses, achieving this level of reliability isn’t just about functionality—it’s crucial to building trust, retaining customers, and preventing reputational and financial damage. In today’s competitive landscape, businesses must demonstrate that their LLM applications meet the highest standards of quality, reliability, and user safety.

Common Challenges in LLM Evaluation

The current landscape of LLM evaluation is far from perfect. Many companies rely on manual “vibe checks” (where human reviewers assess quality without systematic criteria) or LLM-as-a-judge (model grading), which involves using an LLM to assess another LLM’s output. Here’s why these approaches fall short:

Human "Vibe Checks"

• Scalability: Human evaluation doesn’t work at scale, which becomes especially problematic as a production application grows with users. Beyond the sheer impracticality of having humans review high volumes of data, it’s also work that is tedious, repetitive, and not suited to human strengths. For example, a large e-commerce platform relying on human reviewers to evaluate product recommendations would quickly hit bottlenecks, as managing these evaluations manually is both exhausting and unsustainable.
• Consistency: Different reviewers often interpret quality criteria differently, leading to inconsistencies. In customer service applications, for instance, what one reviewer considers a "polite" response might not align with another’s assessment. This subjectivity can result in inconsistent responses for customers, creating a fragmented experience that can erode trust and satisfaction.
• Objectivity: Subjective human judgment can skew results, making it difficult to establish objective benchmarks. In areas like financial advice or healthcare support, subjectivity introduces risk, as inconsistent evaluations can lead to responses that are either too lenient or overly strict, compromising the quality of the output and creating potential compliance issues.

LLM as a Judge

• Difficulty in Interpreting Quality Distributions: LLMs face challenges in accurately interpreting the quality distribution of potential answers, particularly with subjective assessments like “empathy” or “compelling argument quality.” Just as humans rely on detailed mark schemes to evaluate exam answers consistently, LLMs also need highly specific rubrics to avoid ambiguity. For example, distinguishing between a 3/5 and a 4/5 on empathy requires a precise understanding of what these scores mean across contexts—a challenge even human evaluators struggle with. Without clear, objective guidelines for each score level, both LLMs and human evaluators are prone to inconsistency, especially in complex, domain-specific judgments.
• Defining Criteria: For an LLM to evaluate effectively, highly detailed rubrics are required, often involving complex scoring schemes. In practice, developing these rubrics for nuanced applications—such as empathetic communication or persuasive writing—becomes a challenge. It’s not just about creating a list of criteria; it requires defining how to measure aspects like “politeness” or “persuasiveness” across various scenarios. For instance, defining what constitutes “appropriate escalation” in customer support might require detailed situational guidelines that still fall short when applied broadly. Even with such rubrics, LLMs may struggle to apply criteria consistently across real-world scenarios.
• Inflexibility and Lack of Learning: Standard LLMs used as judges do not continually learn from new feedback or human demonstrations of quality. This means they can’t improve their evaluations based on ongoing insights from human reviewers or adapt to evolving quality standards. Instead, these LLMs remain static, requiring manual intervention for any updates or adjustments to improve judgment over time. This inflexibility makes it difficult to ensure the LLM’s assessments evolve to meet changing expectations and new benchmarks, limiting their effectiveness in dynamic, real-world settings.
• Challenges in Production: LLMs are costly and slow to run at scale, making real-time evaluations expensive and impractical. For example, in fast-paced social media moderation, where quick responses are essential, running LLM evaluations on thousands of posts per minute may be prohibitively expensive. Additionally, LLMs often need a ground truth reference for comparison, which isn’t always available in production, especially for subjective qualities like tone or helpfulness in customer service. This lack of adaptability and high cost limit the feasibility of LLMs for production-level evaluation needs.

What Are People Looking for in an Evaluation System?

What makes a good evaluation system?

A high-quality evaluation system should provide consistent, reliable results that meet or exceed expert human judgment, working effectively in both real-time production and offline development settings.

To achieve this, an effective evaluation system includes:

• Real-Time Production Monitoring and Guardrails: Continuous evaluation in production allows the system to monitor outputs as they’re generated, functioning as a guardrail that flags or blocks outputs not meeting quality standards. This helps to prevent subpar responses from reaching users, protecting the user experience and maintaining reliability.
• Offline Development Testing and Optimization: The system should also support offline testing in development, allowing teams to refine models, prompts, and evaluation criteria. This enables unit testing and quality checks within a controlled environment, ensuring consistent improvement and readiness before deployment.
• Integration with CI/CD Pipelines and Regression Testing: Integrating evaluations into CI/CD workflows ensures quality checks at each development stage, catching potential issues early. Additionally, regular regression testing ensures updates don’t degrade performance, maintaining consistent quality across deployments.
• Detailed, Actionable Metrics: Effective evaluation systems provide granular, actionable metrics across dimensions like relevance, adherence to guidelines, factual accuracy, and empathy. These metrics allow for targeted improvements, guiding teams in fine-tuning model outputs effectively.
• Scalability and Efficiency: The system should handle high volumes of data seamlessly, whether evaluating real-time outputs in production or processing extensive offline test data. Scalability is crucial to support both current and future demand without compromising efficiency.
• Absolute Scoring Standards: To ensure fixed quality thresholds, an effective evaluation system should implement absolute scoring rather than relative metrics. Absolute scores allow for clear, objective benchmarks, providing consistent quality thresholds for guardrails and reducing the need for continuous comparisons or complex ranking systems.
• Adaptability and Continuous Learning: Incorporating feedback loops enables the system to learn and improve over time. By adjusting criteria to reflect evolving user needs and quality benchmarks, the system can remain relevant and aligned with changing standards.

What makes a good evaluation metric?

Effective evaluation metrics are foundational to a reliable evaluation system. Metrics should:

• Directly Impact Application Goals: Metrics should align with user satisfaction and functionality, focusing on qualities that are meaningful to the application’s purpose.
• Be Consistently Reliable: Metrics should yield reproducible results across instances, ensuring reliability.
• Detect Subtle Changes: Metrics need to be sensitive to quality shifts, capturing incremental changes accurately.
• Implement Absolute Scoring: Absolute scores provide fixed, objective benchmarks, essential for defining consistent quality thresholds and eliminating complexities from relative comparisons or ranking systems.
• Reduce Subjectivity: Clear, well-defined criteria help maintain consistency with minimal ambiguity.
• Support Improvement: Metrics should deliver insights that directly inform adjustments and refinements.
• Scale with Demand: Metrics should support automated, real-time assessments, ensuring efficiency at scale.

In essence, a good evaluation system, backed by well-structured metrics, supports quality control in production and aids optimization during development, ensuring LLM applications maintain high standards of consistency, adaptability, and user-focused performance.

Key Steps to Achieving Reliable LLM Application Evaluation

Evaluating LLM applications effectively involves defining and implementing a structured approach to gather data, set criteria, and select robust measurement methods. By addressing these steps, you can create an evaluation framework that meets high standards of accuracy, consistency, and relevance to the application’s purpose. Below are three essential questions that can guide a comprehensive LLM application evaluation process.

1. What Dataset Are You Using?

Selecting the right dataset is foundational, as it determines the context and reliability of your evaluation results. There are two primary approaches:

• Online Data (Live Data from Production): This dataset consists of real-time outputs generated in a production environment, allowing for ongoing monitoring of application performance. Online data is valuable for setting up guardrails—such as flagging outputs that deviate from expected norms—thus enabling immediate detection of anomalies or quality degradation.
• Offline Data (Curated Test Inputs): Offline datasets are curated to include diverse, representative test inputs that can assess performance across various conditions. This approach is essential for establishing benchmarks and for conducting unit tests and regression tests over time. Offline datasets help verify that the application meets set standards and performs consistently as it evolves, providing a stable framework for longitudinal evaluation.

2. What Criteria Are You Evaluating?

Defining clear criteria is critical to ensure that the evaluation captures all relevant dimensions of performance. Typically, evaluation criteria fall into three categories:

a) Binary Criteria (Yes/No Questions)

These are straightforward checks that determine if the application meets specific requirements. Binary criteria are useful for factual or rule-based assessments, where answers are clear-cut. Examples include:

• "Is the response a question?"
• "Is the output less than 10 words?"
• "Does it produce valid JSON?"
• "Does it include a specific word or reference the correct source?"

Binary criteria are ideal when you need definitive answers, such as format validation, inclusion of required terms, or adherence to specific rules.

b) Continuous Criteria (Subjective Quality on a Spectrum)

Many real-world applications require more nuanced judgments that cannot be reduced to simple yes/no answers. For these subjective or qualitative aspects, continuous criteria are used to determine how well the application performs along various dimensions. Examples include:

• Relevance and conciseness
• Appropriateness for a specific audience
• Compliance with medical guidelines
• Empathy, helpfulness, or role consistency
• Identification and mitigation of harmful or toxic language

Continuous criteria help capture complex qualities such as helpfulness or appropriateness, which vary by context. Rather than a binary pass/fail, they allow for scoring on a scale, providing more detailed insights into the quality of the output.

c) Accuracy and Hallucination Detection

Evaluating the accuracy of an LLM involves checking whether responses are factually grounded and free from hallucinations (unfounded claims). This category often requires specialized handling due to its complexity. For example:

• Is the answer faithful to the source material?
• Are there ungrounded or speculative claims?

Accuracy criteria are essential for applications where factual reliability is critical, such as legal, medical, or technical information contexts. Evaluating accuracy can protect against misinformation and build user trust by ensuring responses are well-supported.

3. How Do You Measure Each Criterion?

Once criteria are established, the next step is to determine the methods used to measure each criterion effectively.

a) Binary Measurement

Binary criteria are often best handled by a state-of-the-art LLM, acting as a “judge” to assess whether outputs meet specific requirements. For example, an LLM can be queried to confirm if a response meets a simple yes/no criterion, such as JSON validity or question format. This approach works well when criteria are straightforward and when the LLM’s knowledge is sufficient for reliable judgment in the domain.

b) Continuous Measurement

For subjective criteria, automated evaluation should involve a reward model trained on human preferences, combined with guidelines or a “constitution” tailored to the application’s needs. Techniques from reinforcement learning with human feedback (RLHF) and constitutional AI (e.g., as implemented in models like Claude) can help the evaluator align with nuanced, context-specific criteria. This combination enables the evaluator to produce more consistent, human-like judgments along a spectrum of quality.

c) Accuracy Measurement

Assessing accuracy requires a two-part approach, combining statistical metrics with a dedicated accuracy evaluation model to ensure factual consistency and grounding:

• Statistical Metrics: As implemented in the RAGAS framework, you can use statistical methods to triangulate across a response the ground truth, context, and user query. These metrics include:
  • Faithfulness: The percentage of claims in the output that can be directly inferred from the provided context.
  • Context Recall: The extent to which essential information from the ground truth is correctly incorporated in the output.
  • Answer Similarity: How closely the response aligns with an established ground truth answer, capturing content accuracy and structural alignment.
  These statistical metrics provide a data-driven foundation for evaluating factual alignment and completeness in LLM outputs.
• Accuracy Evaluation Model: In addition to statistical methods, a specialized accuracy evaluation model assesses the response’s adherence to factual and contextual standards, addressing criteria such as:
  • Faithfulness to Source Material: Ensures the response accurately reflects provided information without deviating from the context.
  • Guideline Adherence: Verifies that the response aligns with specified standards, maintaining application-specific consistency.
  • Exclusion of Extraneous Information: Flags any additional, unrequested content not derived from the context.
  • Detection of Ungrounded Claims: Identifies unsupported statements or speculative content, ensuring factual integrity.

This combined approach to accuracy measurement ensures reliable evaluation of responses for consistency, factual accuracy, and alignment with source material.

By addressing these three core questions—dataset selection, criteria definition, and measurement methods—an LLM evaluation framework can achieve reliability, scalability, and applicability across diverse, real-world use cases. This structured approach ensures that evaluation results are not only accurate but also actionable and aligned with the application’s intended goals.

What We Do at Composo

At Composo, we focus on providing a precise and scalable evaluation framework tailored for LLM applications. By blending proprietary models with recent research, we focus on three critical areas of LLM evaluation:

1. Hyper-Personalized Evaluation Model

Our Composo Align model is designed to adapt to each application by learning from expert feedback, achieving over 90% alignment with expert judgment—2.6 times better than GPT-4 with LLM-as-a-judge. This high level of agreement ensures that Composo Align consistently reflects expert-level nuance, delivering reliable, contextually appropriate evaluations that meet the unique requirements of each application.

2. Rigorous Accuracy and Hallucination Detection

Our approach to accuracy involves the two-part system outlined above, that combines statistical metrics and a specialized evaluation model, ensuring responses are factually reliable and grounded in context.

3. Simple, Easy-to-Use Platform for Running and Analyzing Evaluations

Our platform offers a straightforward way to run evaluations and access results, with options for different usage needs:

• No-Code Interface: A user-friendly, no-code setup that allows users to directly access evaluation results without technical requirements.
• API Access: An API for easy integration into existing stacks, allowing users to incorporate evaluation capabilities into their workflows wherever needed.

The platform supports both live production data and in-development test data, providing flexibility for real-time monitoring and controlled testing environments.

Getting Started

To learn more about how Composo can support your LLM application evaluations, reach out to us at contact@composo.ai to schedule a discussion.