Can One LLM Judge Another? Exploring the Dynamics of Auto-Evaluation in GenAI

Meeta Dash
3 min readMay 1, 2024

As AI systems grow more complex, the demand for efficient evaluation methods becomes crucial. Auto-evaluation, where AI models automatically assess the outputs of other AI models, is emerging as a popular technique. This method not only enhances efficiency but also introduces important considerations for companies developing generative AI (GenAI) products. Drawing from my experience working with both startups and large enterprises, a key challenge has been determining the best way to evaluate large language models (LLMs). The discussion often centers on the balance between automated evaluations and the need for human oversight.

Understanding Auto-Evaluation:

Auto-evaluation involves one AI model assessing another’s output, which can range from simple text comparisons to employing various prompting strategies to compute quality metrics.. Auto-evaluation allows for scalability and efficiency in assessing AI-generated outputs. It’s an efficient way to filter through vast amounts of data generated by LLMs, identifying clear errors or outliers quickly.

Trusting AI to Evaluate AI:

It may initially seem odd to use one large language model (LLM) to evaluate another’s output. However, this approach is effective due to the asymmetry between generating and evaluating tasks. Evaluators do not need to replicate the creative or generative capabilities of the primary LLM. Their main role is to verify the accuracy and relevance of the content, often utilizing additional tools and data to aid their decisions.

Moreover, some tasks are inherently simpler to verify than to create. For instance, assessing the quality of a generated press release is typically more straightforward than producing it. The generation process requires the LLM to consider multiple factors — such as the target audience, company specifics, and the objectives of the press release — making it complex. In contrast, an evaluator LLM reviews the text against a set standard of quality metrics like toxicity, verbosity, and conciseness.

Selecting the Right Auto-Evaluation Frameworks:

There is a diverse array of auto-evaluation frameworks available, including many open-source options such as RAGAs, DeepEval, llamaindex, and Guardrails. Each framework brings its own set of benefits and challenges, and experimenting with different options can help identify the most effective approach for specific use cases. Beginning with simpler evaluators, such as those measuring toxicity or verbosity, provides a strong foundation. This strategy allows you to build experience and confidence, preparing you for more complex evaluation systems.

Integrating Human Expertise:

Despite automation’s efficiencies, the unpredictable nature of generative AI necessitates human oversight, particularly in critical situations. Human evaluators offer a nuanced understanding of context and quality that LLMs cannot fully mimic, crucial when ‘quality’ in generated content is subjective and varies by use case.

Additionally, humans are vital in defining and refining the quality parameters used to assess LLM outputs. Through iterative QA processes, human evaluators can pinpoint issues, gather insights, and fine-tune evaluation frameworks to better align with user needs and operational standards.

Iterative Refinement:

Implementing auto-evaluation is not a one-time endeavor but rather an iterative process of refinement. You should continuously evaluate and refine auto-evaluation pipelines, incorporating feedback from both automated and human evaluators. For more critical quality needs, dual LLM checks can be employed where two LLMs evaluate the outputs; if they disagree, the content is escalated to a human evaluator. Additionally, a continuous learning system can be implemented, utilizing human feedback to train a smaller, specialized LLM. This model adapts and improves over time, learning from real-world applications and feedback, thereby enhancing its evaluation capabilities.

Auto-evaluation is a promising tool for enhancing the development and reliability of AI models. By understanding its nuances, choosing appropriate frameworks, incorporating human judgment, and continuously refining the process, companies can effectively leverage auto-evaluation to advance their AI initiatives.

Three Important Considerations for Auto-Evaluation:

  • Begin with simple evaluators and increase complexity gradually.
  • Incorporate human-in-the-loop evaluations for detailed judgment and insights tailored to specific business needs.
  • Continuously improve and adapt auto-evaluation processes, experimenting with various frameworks and strategies for better outcomes.



Meeta Dash

A customer-centric product leader. Love to share my thoughts on product innovation, new technologies and leadership.