The article discusses the challenges of training language learning models (LLMs) for software development tools. Developers have limited influence over LLM outputs unless they own the models, raising concerns about bias and accuracy in the information provided by these tools. Organizations like MongoDB collaborate with cloud services such as AWS to refine LLM training with high-quality examples. The article highlights the need for continuous evaluation of LLM performance to ensure they guide developers effectively while emphasizing the opaque nature of achieving reliable guidance from these models.
For example, in my role running developer relations for MongoDB, we've worked with AWS and others to train their LLMs with code samples, documentation, etc.
Microsoft's Victor Dibia delves into this, suggesting, "As developers rely more on codegen models, we need to also consider how well does a codegen model assist with a specific library/framework/tool."
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