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Noisy Text in RAG: Typos, OCR, and the Gap Classical Spell-Check Leaves

Enterprise Document Intelligence [Vol.1 #B1] - Three sources of one problem. User typos, fast-typing transcription noise, OCR character errors. Classical spell-check handles one of them. Embeddings carry the rest

The post Noisy Text in RAG: Typos, OCR, and the Gap Classical Spell-Check Leaves appeared first on Towards Data Science.

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RAG Is Not the Whole Toolkit: The NLP Techniques Real Problems Still Need

Enterprise Document Intelligence [Vol.1 #B00] - Retrieval answers one kind of question. Classifying a request, matching free text to a reference list, reading a table, cleaning OCR noise: each has a cheaper method that works, and the engineering is knowing which one to reach for

The post RAG Is Not the Whole Toolkit: The NLP Techniques Real Problems Still Need appeared first on Towards Data Science.

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How Does a RAG Reranker Really Work?

Enterprise Document Intelligence [Vol.1 #2D] - What data scientists say when asked, what the model actually does under the hood, and why the honest answer changes your architecture decisions in enterprise RAG

The post How Does a RAG Reranker Really Work? appeared first on Towards Data Science.

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