Train your model to do things your way
Travis Rehl, CTO at Innovative Solutions, says what generative AI tools need to work well is “context, context, context.” You need to provide good examples of what you want and how you want it done, he says. “You should tell the LLM to maintain a certain pattern, or remind it to use a consistent method so it doesn’t create something new or different.” If you fail to do so, you can run into a subtle type of hallucination that injects anti-patterns into your code. “Maybe you always make an API call a particular way, but the LLM chooses a different method,” he says. “While technically correct, it did not follow your pattern and thus deviated from what the norm needs to be.”
A concept that takes this idea to its logical conclusion is retrieval augmented generation, or RAG, in which the model uses one or more designated “sources of truth” that contain code either specific to the user or at least vetted by them. “Grounding compares the AI’s output to reliable data sources, reducing the likelihood of generating false information,” says Mitov. RAG is “one of the most effective grounding methods,” he says. “It improves LLM outputs by utilizing data from external sources, internal codebases, or API references in real time.”
Many available coding assistants already integrate RAG features—the one in Cursor is called @codebase, for instance. If you want to create your own internal codebase for an LLM to draw from, you would need to store it in a vector database; Banerjee points to Chroma as one of the most popular options.
