Retrieval pipelines, vector search, chunking and reranking: how models are grounded in a corpus instead of their weights.
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.
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.
Most RAG systems are optimized for answer quality, not cost—and that blind spot gets expensive fast. In this article, I break down a production-ready cost control layer combining semantic caching, query routing, token budgeting, and circuit breaking, achieving an 85% reduction in LLM costs without sacrificing answer quality.
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--> Understanding the behavior of complex machine learning systems, particularly Large Language Models (LLMs), is a critical challenge in modern artificial intelligence. Interpretability research aims to make the decision-making process more transparent to model builders and impacted humans, a step toward safer and more trustworthy AI.
OpenAI takes an ownership stake in Thrive Holdings to accelerate enterprise AI adoption, embedding frontier research and engineering directly into accounting and IT services to boost speed, accuracy, and efficiency while creating a scalable model for industry-wide transformation.
What exactly does word2vec learn, and how? Answering this question amounts to understanding representation learning in a minimal yet interesting language modeling task.
Discover how Blue J is transforming tax research with AI-powered tools built on GPT-4. 1.