In the rapidly evolving landscape of LLM applications, reliable evaluation remains the critical bottleneck for deployment confidence. Current approaches—particularly using LLMs as judges—introduce significant uncertainty into development cycles, with inconsistent scores and poor correlation to real-world performance metrics.
At Composo, we've developed a fundamentally different approach - Composo Align. Underpinned by a generative reward model architecture, it provides deterministic, consistent scoring that enables teams to quantify improvements with confidence. Here we outline how we've validated our approach across both internal customer datasets and real-world benchmarks that matter for production applications.
Why LLM as a judge falls short
LLM-based evaluations can be adequate in scenarios where high-level checks suffice for your needs. They're a reasonable option when you don't require reliable, quantitative results for decision-making and when you have time to invest in optimizing the evaluation process.
However, if quality of application outputs is a crucial factor for you, you'll need to look beyond standard LLM as a judge approaches. Alternatives like Composo become essential when you need evaluation results you can truly trust and depend on. These solutions are particularly valuable when you want a system that's quick to implement, and straightforward to use, eliminating the complexity and inconsistency inherent in traditional LLM-based evaluation methods.
Composo is the alternative to LLM-as-a-judge, which:
Composo Align achieves outstanding performance compared to the next best state of the art evals:
In addition, Composo’s architecture makes its scoring deterministic, resulting in 100% consistency & repeatability of scores. LLM as a judges on the other hand demonstrate significant variation when evaluating the exact same datapoints.
Instead of relying on artificial benchmarks, we've validated our generative reward model approach using methodologies focused on real-world applicability. In doing so we can demonstrate how our model performs in actual production environments, delivering consistent scores aligned with business metrics.
Our primary validation instrument, the PrimeBench benchmark, was constructed to address the limitations of conventional academic evaluation methods. This curated dataset integrates diverse domain-specific challenges in real-world industry tasks ranging from finance to healthcare, news summarisation & customer support.
It incorporates data from a range of sources, such as:
PrimeBench contains complex queries that reflect actual business scenarios requiring domain expertise, contextual understanding, and retrieval capabilities. Each domain presents unique challenges that test different aspects of model performance:
Financial Analysis (FinQA):
Medical Research (PubMedQA):
Technical Support (TechQA):
"Can you help me find a list of all the versions and fixpacks of the ITCAM Agent for Datapower and where can I download it?" - Tests product knowledge, technical documentation retrieval, and practical resource identification.XSum (Cross-domain):
These examples illustrate why effective evaluation requires a benchmark that is able to assess response quality in contexts that matter to businesses, where use cases span multiple knowledge domains and contain detailed contexts from complex, proprietary knowledge sources.
Beyond our publicly available benchmark dataset, we supplemented our analysis with anonymized production data from consenting enterprise partners across multiple sectors. These datasets represent authentic customer interactions and business use cases from organizations that have implemented our evaluation framework.
This real-world corpus provides a critical validation layer that confirms our findings generalize to production environments with actual business metrics and success criteria.
To establish a comprehensive performance baseline, we evaluated Composo Align against the most advanced LLM-as-judge implementations available as of May 2025 & RAGAS. Our comparison utilized the following models, both configured with specialized judgement prompts optimized for evaluation task:
For each test case in both PrimeBench and our supplemental datasets, we conducted pairwise comparisons between Composo Align and these state-of-the-art LLM judges.
You can find the PrimeBench dataset here & implementation for replication here.
A key advantage of our approach is dramatic simplification of the evaluation process. While LLM judges require complex prompt engineering, our generative reward model needs only a single-sentence criterion.
For example, to evaluate empathy in customer support responses, you simply define:
Or to evaluate faithfulness:
This simplicity enables:
The path to production-ready LLM applications requires evaluation metrics teams can trust. Our generative reward model approach delivers precisely this: consistent, deterministic scores that enable confident development decisions.
By focusing our validation on real-world datasets and applications rather than artificial benchmarks, we've demonstrated superior performance where it matters most—in the complex, diverse domains where LLMs are actually deployed.
For teams building LLM applications, this means:
