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News
One Document Type, a Million Files: Structured Extraction into the SQL Table RAG Queries
2+ week, 6+ day ago (1842+ words) Enterprise Document Intelligence [Vol.1 #14C] - One hour with two people, six to ten fields, and the two signals that separate a real column from one that will break a filter later A lot of RAG work right now goes into letting…...
Retrieve One Row from a Table, Not the Whole Table: Row-Level Chunks for RAG
3+ week, 3+ day ago (928+ words) Enterprise Document Intelligence [Vol.1 #7sexies] – The unit of retrieval doesn’t have to be a page or a paragraph. When the corpus carries tables, each body row with its column headers is a chunk in its own right, and it’s often the…...
How to Fine-Tune an LLM: An End-to-End Guide
3+ week, 4+ day ago (1726+ words) A hands-on guide to fine-tuning LLMs for the real world We fine-tuned a 7B parameter model which completely blows foundation models out of the water, but just for this very narrow subtask: Filling out synoptic reporting templates for breast cancer. This…...
Kimi K3???s 1M Token Context Window vs. RAG: Cost, Latency and Answer Quality
3+ week, 5+ day ago (1857+ words) A controlled comparison of a top-5 RAG pipeline and a full 127,000 token prompt on the same 12 questions, same system prompt and same model. Graded blind on correctness, completeness and grounding. When Kimi K3 came out in July with a context window…...