Many RAG accuracy issues come from search, not the model
RAG systems often give wrong answers because they receive the wrong information, not because the model itself is inventing . The model usually answers from the material it has been given. If that material is weak, missing, or poorly matched to the question, the answer will also be weak.
The biggest gains can come from splitting documents by their real structure instead of fixed sizes, adding after , and building a test set from real user questions. Changing models may matter less than improving how the system finds the right information. do not repair bad input; they can make a wrong answer sound more convincing.
GraphRAG can help with complex questions that span many connected documents, but simple document questions may be solved by better splitting and search before adding a more complicated system.
Key points
- Bad RAG answers often start with poor , not poor writing by the model.
- Documents should be split by structure, not only by fixed length.
- after can improve which information reaches the model.
- A test set should come from real user questions, not guesses.
- GraphRAG is better saved for questions that need links across many documents.