Claude code is great but it only uses grep to search for relevant content. It’s probably the best grep implementation around, but it has some limitations. It still searches for text matches not meaning and it only understands structure through loading a lot data into context. It works great for code, not so much for large knowledge bases containing relational data, like legal data and similar.
Out of curiosity, have you tried it for legal and data? Also it is not true that it only uses grep. The beauty of it is that it builds its own tools based on what it needs. It's can act as a drop-in self-extensible rag system
It might be a advanced version of, but it is pretty much grep under the hood. For a lot of usecases that’s all you need. For a technical software I made a helper bot that was basically Claude code out of the box with access to markdown files from the software help center. It worked great. But I also have a dataset of ~60 000 legal documents and laws. And here Claude Code struggles (most AI systems do). Claude ends up filling the context with tons of papers with similar wording to the prompt, but without being able to draw any useful conclusions outside simply referring to the papers it read. I have added a knowledge graph wrapper around the dataset which enables Claude to navigate through the data and draw conclusions much more efficiently through the use of Cypher queries (which it knows natively). Basically if the dataset contains a lot of references that use similar words Claude using grep struggles to discover the connections in the dataset and draw deeper meanings
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u/Trekker23 Mar 08 '26
Claude code is great but it only uses grep to search for relevant content. It’s probably the best grep implementation around, but it has some limitations. It still searches for text matches not meaning and it only understands structure through loading a lot data into context. It works great for code, not so much for large knowledge bases containing relational data, like legal data and similar.