Enterprise Document Intelligence: A Series on Building RAG Brick by Brick, from Minimal to Corpus scale
The article discusses the challenges and misconceptions surrounding Retrieval-Augmented Generation (RAG) in enterprise document intelligence. It emphasizes that successful implementations require a deep understanding of the business domain and the specific documents involved, rather than just relying on advanced tools and models. The author proposes a simplified approach that focuses on document and question parsing, retrieval, and generation to improve the reliability of answers provided by AI systems.
- ▪Generative AI and RAG have become standard solutions for querying documents in enterprises.
- ▪Many deployments of RAG systems have resulted in disappointing outcomes, with users often distrusting the answers provided.
- ▪The author argues that a better understanding of the documents and the domain is crucial for effective implementation, rather than just improving infrastructure and tools.
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Large Language Models Enterprise Document Intelligence: A Series on Building RAG Brick by Brick, from Minimal to Corpus scale For AI engineers who want to understand every step, not just call the library angela shi May 22, 2026 25 min read Share About three years ago, generative AI took off and RAG showed up as the standard answer for “we have documents, we want to ask questions.” The pitch sounded miraculous. The implementation everyone described was the same one, over and over: chunk the documents, push the chunks into a vector store, embed the question, retrieve top-k by cosine similarity, optionally rerank, send the hits to an LLM Vendors converged on it. Consulting decks converged on it. Conference talks converged on it.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at Towards Data Science.
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