Imagine having an AI assistant that could instantly answer any coding question, pulling information directly from massive code repositories. That's the promise of repository-level question answering (QA), a rapidly evolving field in AI. Researchers are pushing the boundaries of what's possible, but how effective are these AI assistants in the real world? A new, massive benchmark called CodeRepoQA aims to find out. This benchmark dives deep into the complexities of real-world coding scenarios, using a vast dataset of over 585,000 multi-turn dialogues from popular GitHub repositories across five programming languages (Python, Java, TypeScript, JavaScript, and Go). Unlike previous single-turn QA datasets, CodeRepoQA reflects how developers actually solve problems—through back-and-forth discussions and iterative refinements. So, how do current large language models (LLMs) fare against this challenge? The research shows a mixed bag. While some LLMs performed surprisingly well, even the best models struggled to consistently provide accurate and comprehensive answers. Interestingly, the length of the question played a significant role in performance. Medium-length questions proved the sweet spot, while extremely short or long questions tripped up the models. This suggests LLMs still struggle with context management and information synthesis. While there's a long way to go before AI can truly replace human expertise, CodeRepoQA provides a valuable testing ground for future improvements. It highlights the need for more sophisticated models that can understand complex code interactions and engage in nuanced technical discussions. The future of AI-powered coding assistants hinges on overcoming these challenges, and benchmarks like CodeRepoQA will pave the way for more robust and reliable AI tools.