Insights from Developing AI Coding Assistants: Strategies for Context Retrieval and Evaluation

This paper, presented at RecSys ‘24, discusses the significant role of context in transforming large language models (LLMs) into effective coding assistants. While LLMs can generate generic responses based on training data, they require contextual knowledge of specific codebases to provide relevant, accurate solutions. The paper introduces Sourcegraph’s context engine, which retrieves and ranks relevant snippets from a wide array of context sources, enhancing LLM responses through in-context learning. The context engine employs a two-stage process of retrieval and ranking to ensure that the most pertinent information is included within token budget constraints. Challenges in evaluating the effectiveness of context retrieval and LLM responses are also addressed, emphasizing the need for expert annotations and feedback mechanisms. Ultimately, the paper aims to improve AI-assisted software development by providing developers with tailored, accurate assistance, and invites further exploration through the detailed findings available in their arXiv publication.

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