and expensive. The fast parser misses the table the answer lives in. Run the heavy one on every page of a 200-page report and you pay for 200 pages to…
, I will give my first impressions of the newest OpenAI model, GPT-5.6. The model was released a few days ago, and I’ve gotten the chance to test it extensively…
talk about context engineering today, they usually mean retrieving the right context from a document: chunk selection, hybrid search, reranking, TOC-aware retrieval. That is one half of the story. The…
application we build is a RAG app. The recipe is simple: chunk, embed, retrieve, then answer. It looks clean on paper. But once you use it on real cases, things…
Hallucinations have been an ongoing problem since OpenAI first introduced its ChatGPT chatbot in November 2022, highlighting generative AI’s tendency to generate plausible but false or misleading information and its…
I’ve worked on, conversation state tends to grow quickly over time. It’s common to resend large portions of the history on each turn—including older tool outputs, repeated RAG retrievals, and…
is straightforward. You load the weights, load the data, and wait for it to finish. For most models, that’s fine, and the only real cost is time. When the wait…
some of the problems we face in implementing AI algorithms, we usually focus on the processors’ ability to handle them. But now, NVIDIA’s GPUs power the latest large language models,…
Retrieval-Augmented Generation (RAG) has evolved rapidly in recent times. The original RAG paradigm was designed to be straightforward: retrieve the most relevant chunks from a corpus and use them to…
part of the generation brick of Enterprise Document Intelligence, a series that builds an enterprise RAG system from four bricks: document parsing, question parsing, retrieval, and generation. Article 8A (the…
