Large Language Models (LLMs) are rapidly changing the landscape of software development, offering the potential to automate tasks, boost efficiency, and even help novice programmers learn the ropes. But how effective are these AI assistants in real-world coding scenarios? Researchers at Prosus AI recently tackled this question head-on with a new set of benchmarks designed to put LLMs through their paces. Their research introduces two new benchmark datasets: StackEval, a comprehensive collection of real questions and answers from Stack Overflow covering 25 programming languages and various coding tasks (debugging, implementation, optimization, and conceptual understanding), and StackUnseen, a dynamically updated dataset featuring the latest questions from Stack Overflow, specifically designed to test LLMs against emerging coding challenges not present in their training data. The results are illuminating. While LLMs excel at answering questions based on established coding practices and historical data (achieving remarkably high acceptance rates of up to 95.5% on StackEval), their performance dips significantly when confronted with newer, unseen challenges. This performance gap underscores the challenge of generalization in AI—the ability of a model to apply its knowledge to new, unfamiliar situations. Interestingly, the research suggests a correlation: LLMs that perform well on established problems tend to handle novel challenges more effectively. This indicates that the fundamental capabilities driving strong performance on common tasks also contribute to better adaptability. Beyond simply testing the coding prowess of LLMs, the Prosus AI team also investigated how well these models can act as judges for evaluating code. This “LLM-as-a-Judge” benchmark explores whether LLMs can accurately assess the quality and correctness of code solutions. The findings suggest that LLMs, when provided with a reference solution, demonstrate a surprisingly high level of accuracy (up to 84.4%) in judging the acceptability of generated code. This has significant implications for automating code review processes, potentially freeing up developer time for more creative tasks. Interestingly, simply prompting the LLM to use “chain-of-thought” reasoning without a reference solution actually decreased accuracy, suggesting that contextual information is crucial for effective judgment. Finally, the research addresses the issue of “self-preference” bias, where LLMs might favor code they generated themselves. In the coding domain, this bias appears to be minimal, particularly when reference solutions are available. This objectivity is likely due to the inherent nature of coding tasks, where correctness can be objectively evaluated against functional requirements. The implications of this research are far-reaching. While LLMs show remarkable promise as coding assistants, the ability to adapt to the ever-evolving landscape of software development remains a significant challenge. Further research is needed to develop new techniques that improve generalization and adaptability, bridging the gap between historical knowledge and emerging challenges. The Prosus AI team’s contribution provides a valuable framework for evaluating and improving LLMs, paving the way for more effective and reliable AI-powered coding tools.