The current approach to designing LLMs within AI engineering is oversimplified. According to the echo chamber’s view, solving LLM hallucinations is easy: simply design a standard Retrieval-Augmented Generation (RAG) system in which you break your PDFs into 1,000-token chunks, embed them, insert them into a vector database, and perform cosine similarity searches.

After deployment, companies quickly discover that using only chunked text for retrieval does not work well for complex questions. The standard RAG assumes that semantic similarity implies relevance, which is not necessarily the case. When users ask a “multi-hop” question that requires making connections between Concept A and Concept B through Concept C, standard RAG will not work because these concepts usually don’t coexist in the same chunk of text. RAG also falls apart at global summarization (“what are the major risk factors discussed in all of our compliance reports?”)

“The standard RAG assumes that semantic similarity implies relevance, which is not necessarily the case.”

If you are designing AI for enterprise systems, you need structured reasoning. Chunking text is fine, but you should stop doing it randomly. What you need is GraphRAG.

GraphRAG offers a powerful combination of the structural knowledge of knowledge graphs along with the semantic capabilities of vector search. This tutorial will explain how your current pipeline fails and help you implement a GraphRAG workflow using Python.

We will now take apart a simple misconception that vector embeddings will save the day!

Assume you have a dataset of corporate contracts stored in a vector database.

Source link