GitHub Copilot Chat's capabilities are constrained by its underlying model. One primary limitation is that it is trained on a static, albeit massive, dataset. This means its knowledge has a specific cutoff date, and it is unaware of new libraries, frameworks, security vulnerabilities, or API changes that have occurred since its training. This is what is meant by "Limited training data" (A).

Furthermore, while Copilot supports numerous programming languages, its performance is not uniform across all of them. It is explicitly optimized for a subset of popular languages (like Python, JavaScript, C++) where training data is abundant. For less common languages, the quality and relevance of suggestions are significantly lower, meaning it does not have "Extensive support for all programming languages" (D).

B. Ability to handle complex code structures: This is phrased as a strength. While its ability to understand complex code is imperfect and can be a limitation, the statement itself is not presented as a limitation.

C. No biases in code suggestions: This is a false statement. Official documentation explicitly warns that because the training data includes code from public repositories, Copilot can reproduce biases and insecure coding patterns found in that data.

1. GitHub Docs, "About GitHub Copilot": This document clarifies the training data and language support. It states, "For each language, the quality of suggestions you receive may depend on the volume and diversity of training data for that language... Languages with less representation in public repositories may be less supported." This directly supports that the lack of extensive, uniform support for all languages is a limitation (related to option D).

2. GitHub Docs, "Frequently asked questions about GitHub Copilot": This page addresses the model's knowledge base. It explains that the model is trained on a specific corpus of data, which implies a knowledge cutoff. The documentation also notes, "GitHub Copilot may suggest old or deprecated uses of libraries and languages," which is a direct consequence of its static, or "limited," training data (supporting option A).

3. Barke, P., et al. (2023). Grounded Copilot: How Programmers Interact with Code-Generating Models. arXiv preprint arXiv:2305.16337. (This is a peer-reviewed academic paper from Stanford University). Section 2.1, "Background on LLM-based Code Generation," discusses the nature of training data for models like Copilot, noting they are trained on static snapshots of repositories, which inherently limits their knowledge to the timeframe of the data collection. This supports the concept of "Limited training data" (A) as a key limitation.