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Best practices to build generative AI applications on AWS

AWS Machine Learning Blog

Building large language models (LLMs) from scratch or customizing pre-trained models requires substantial compute resources, expert data scientists, and months of engineering work. Agents FMs can understand and respond to queries based on their pre-trained knowledge.

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Copyright, AI, and Provenance

O'Reilly Media

Another group of cases involving text (typically novels and novelists) argue that using copyrighted texts as part of the training data for a Large Language Model (LLM) is itself copyright infringement, 1 even if the model never reproduces those texts as part of its output.

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What is Retrieval Augmented Generation (RAG)?

Pickl AI

This collaboration bridges the gap between static knowledge models and dynamic query resolution, ensuring relevance and fluency. Coupled with the demands of running large language models, latency becomes a major concern. Slow response times can undermine user experience, making optimisation critical.

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The Hallucination Problem of Large Language Models

Mlearning.ai

Hallucination in the context of language models refers to the generation of text or responses that seem syntactically sound, fluent, and natural but are factually incorrect, nonsensical, or unfaithful to the provided source input. Furthermore, they have been shown to possess an impressive ability to generate fluent and coherent text.

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Anthropic Claude 3.5 Sonnet ranks number 1 for business and finance in S&P AI Benchmarks by Kensho

AWS Machine Learning Blog

Sonnet currently ranks at the top of S&P AI Benchmarks by Kensho , which assesses large language models (LLMs) for finance and business. 1,614,762 $1,625,687 $1,586,008 Domain knowledge Models must demonstrate an understanding of business and financial terms, practices, and formulae. Anthropic Claude 3.5