From Regex to Vision Models: Which RAG Technique Fits Which Problem

Enterprise Document Intelligence [Vol.1 #3] - Why the ML toolkit (hyperparameter sweeps, train/test splits, explainability frameworks) solves the wrong problem, and what to use instead The post RAG Is Not Machine Learning, and the ML Toolkit Solves the Wrong Problem appeared first on Towards Data Science.

Enterprise Document Intelligence [Vol. 1 #2bis] Why stacking a reranker on top of weak retrieval doesn’t save it, what cross-encoders actually fix vs what they don’t, and where the editorial position of the series lands. The post Rerankers Aren’t Magic Either: When the Cross-Encoder Layer Is Worth the Cost appeared first on Towards Data Science.

Enterprise Document Intelligence [Vol. 1 #2] Why the same vector search that handles synonyms and paraphrase silently fails on negation, exact identifiers, and your company’s acronyms, and what to use when it does. The post Embeddings Aren’t Magic: The Predictable Failure Modes of RAG Retrieval appeared first on Towards Data Science.

Enterprise Document Intelligence [Vol. 1 #1] The smallest version of RAG that actually works, on a real PDF, with grounded answers and the source lines highlighted. The post Baseline Enterprise RAG, From PDF to Highlighted Answer appeared first on Towards Data Science.

How I turned 100 messy pdfs into structured insights by building a deterministic loop around agents The post Stop Using LLMs Like Giant Problem Solvers appeared first on Towards Data Science.

For AI engineers who want to understand every step, not just call the library The post Enterprise Document Intelligence: A Series on Building RAG Brick by Brick, from Minimal to Corpus scale appeared first on Towards Data Science.

GRASP is a new gradient-based planner for learned dynamics (a “world model”) that makes long-horizon planning practical by (1) lifting the trajectory into virtual states so optimization is parallel across time, (2) adding stochasticity directly to the state iterates for exploration, and (3) reshaping gradients so actions get clean signals while we avoid brittle “state-input” gradients through high-dimensional vision models. Large, learned world models are becoming increasingly capable. They can predict long sequences of future observations in high-dimensional visual spaces and generalize across tasks in ways that were difficult to imagine a few years ago. As these models scale, they start to look less like task-specific predictors and more like general-purpose simulators. But having a powerful predictive model is not the same as being able to use it effectively for control/learning/planning. In practice, long-horizon planning with modern world models remains fragile: optimi

Learn how to upload and work with files in ChatGPT to analyze data, summarize documents, and generate content from PDFs, spreadsheets, and more.