I've been using Claude Code along with some custom libraries we've built across different repositories, but I'm running into issues where it keeps hallucinating—basically, it's generating code with incorrect syntax or referencing methods that don't exist. The main problem seems to be that Claude doesn't recognize our custom implementations and is trying to guess how our components work based on standard libraries, which are definitely missing our unique company specifications.
For context, we have:
- Private React components that are documented in Storybook, and
- Custom Terraform modules with documentation in Terrareg.
Although we have both the documentation and the source code, the scope is too broad for Claude's context window. Because of this limited context, it defaults to standard implementations, leading to even more confusion.
I'm curious if anyone else has tackled this issue? Could an MCP (Model Control Protocol) help, and if so, what MCP servers would you recommend for handling private libraries? What strategies have worked for you when dealing with AI coding assistants and private component libraries?
5 Answers
You might want to check out Vector RAG—it really helps in situations like yours. Instead of Claude guessing, you can index your documentation and code. When you ask a question, it’ll pull the most relevant snippets, which should reduce the hallucination issue. Not a perfect fix for complex architectures, but great for more straightforward calls to methods and props! Look into using LlamaIndex, LangChain, and maybe Pinecone or Weaviate.
A practical approach to consider is breaking down your queries into smaller, more specific questions. There's really no need for Claude to have the whole library for every task. Just provide function signatures or a snippet of the relevant library code to give it context—it might help improve accuracy!
Just tell it to stop hallucinating! 😅 Seriously though, I feel your pain—it can be frustrating!
This problem is definitely a tough nut to crack! LLMs (Large Language Models) struggle with large codebases, as you’ve noticed. Have you thought about fine-tuning a model specifically on your codebase? That might help it understand your library better!
Another angle could be to use a different LLM that offers a larger context window to summarize the libraries into a markdown file, which you can then feed to Claude. Maybe something like Gemini could work?
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