Biggest Misconception In Rag
Why You’re Getting It Wrong with RAG (and How to Fix It)
If you think you can just throw questions at a RAG (Retrieval-Augmented Generation) pipeline and expect it to give you the perfect answer, you’re not alone—but you’re also mistaken. There’s a common misconception that all you need is a vector search of questions against documents, and voilà, you’ll get relevant results. Unfortunately, this can lead to something called “semantic dissonance”—the model just doesn’t get what you were originally asking.
Here’s what your RAG pipeline should actually look like if you want results that make sense.
1. Match Apples to Apples
Instead of matching questions directly with documents (which is like comparing apples to oranges), you should be comparing similar data types. This means comparing questions to other questions, not raw documents.
The best way to do this is by creating a set of example questions for each document. When you search, you’re not just throwing a question into the void—you’re matching it with a similar question from the set you’ve prepared.
2. Use Domain-Specific Embeddings
Generic embeddings won’t cut it. Your documents belong to a specific domain, so the embeddings you use should be tailored to that domain. This improves the relevance of your search results, so you don’t end up with random or irrelevant information.
3. Go Beyond Cosine Similarity
Don’t just rely on cosine similarity to rank your retrieved documents. You’ll get better results if you also take into account other metrics like user feedback or how popular a document is. Just because two vectors are “close” doesn’t mean the document is actually useful.
4. Set Smart Thresholds
Cosine similarity values can be tricky. You need to really understand what a good or bad cosine similarity value means for your specific use case. Once you do, you can set thresholds that make sense for your pipeline and filter out the junk.
By following these steps, you’ll get better results from your RAG pipeline and avoid the frustration of irrelevant answers. After all, it’s not just about finding an answer—it’s about finding the right one.