#enterprise document intelligence
Enterprise Document Intelligence: 7 AI articles covering enterprise document intelligence news, analysis, and research
Articles
FAQ as RAG: When You Get to Design the Corpus⭐10
FAQ as RAG rethinks retrieval when the corpus is Q&A pairs: parsing becomes trivial, retrieval works as a cache, and few-shot prompting turns into a lookup prob...
Noisy Text in RAG: Typos, OCR, and the Gap Classical Spell-Check Leaves⭐10
How typos, OCR errors, and transcription noise break RAG retrieval—and why classical spell-check falls short, leaving embeddings to bridge the gap.
Multi-Document RAG: A Folder of Unrelated PDFs Is One Long Document with a Nested Outline⭐9
Learn how to scale RAG across unrelated PDFs using a nested outline and router, bypassing the need for shared metadata fields.
Three Kinds of RAG Corpus, and What It Costs to Build for the Wrong One⭐9
Discover the three RAG corpus types—unrelated reports, duplicate contracts, and claim folders—and what building for the wrong one costs.
Prompt, Context, Loop: The Three Engineering Layers Every RAG⭐10
Explore the three engineering layers of RAG systems: prompt, context, and loop. Learn how each layer works and why their evolution isn't a simple sequence.
Loop Engineering for RAG Generation: An LLM Cascade from a Cheap⭐8
Optimize RAG generation costs by cascading from a cheap local model to a hosted flagship, escalating only when validation fails.
Most RAG Hallucinations Are Extraction Errors: Seven Patterns⭐9
RAG failures are often extraction mistakes, not true hallucinations. Use typed generation contracts with seven patterns to enforce correct answer shapes and red...