Enhancing AI Coding Assistants with Context Using RAG and SEM-RAG

Basic AI coding assistants, while helpful, often fall short of delivering the most relevant and contextually accurate code suggestions due to their reliance on a general understanding of software languages and the most common patterns for writing software. These coding assistants generate code that is suited to solve the problems they are tasked with but are often not aligned with an individual team’s coding standards, conventions, and styles. This typically results in the need to modify or refine their recommendations in order for the code to be accepted into the application.

AI coding assistants typically function by leaning on the knowledge contained within a specific large language model (LLM) and applying general coding principles across various scenarios. Therefore, typical AI assistants often lack the ability to understand the specific context of a project, leading to suggestions that, while syntactically correct, might not align with a team’s unique guidelines, expected approach, or architectural designs. The LLMs that underpin generative AI systems operate based on a fixed set of training data, which does not evolve dynamically as the project progresses. This static approach can result in mismatches between the generated code and the project’s current state or requirements, necessitating developers to make further manual adjustments.

Read the entire article at?The New Stack

Janakiram MSV?is an analyst, advisor, and architect. Follow him on?Twitter,??Facebook?and?LinkedIn.