Building with LLMs? Don’t Ship Without This Evaluation Guide
Last Updated on August 29, 2025 by Editorial Team
Author(s): Claudia Ng
Originally published on Towards AI.
What the GPT-4o rollback tells us about building smarter AI tools
On April 28th, OpenAI quietly rolled back a ChatGPT update just three days after release. Users reported that the new update made GPT-4o overly agreeable, often echoing their opinions even when they were clearly wrong (you can read their post here).
This article discusses the complexities of evaluating language learning models (LLMs), emphasizing how their evaluation differs from traditional machine learning due to the nuanced nature of free-form text generation. It highlights various evaluation methods, including code-based benchmarks, traditional NLP metrics, human evaluations, and LLM-as-a-judge approaches, stressing the importance of considering qualitative dimensions such as hallucination and robustness. Ultimately, the piece argues for incorporating comprehensive evaluation strategies into the development process of AI tools to ensure reliability and user alignment.
Read the full blog for free on Medium.
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Note: Article content contains the views of the contributing authors and not Towards AI.
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