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Build knowledge-powered conversational applications using LlamaIndex and Llama 2-Chat

AWS Machine Learning

Unlocking accurate and insightful answers from vast amounts of text is an exciting capability enabled by large language models (LLMs). With these state-of-the-art technologies, you can ingest text corpora, index critical knowledge, and generate text that answers users’ questions precisely and clearly.

APIs 101
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Deploy self-service question answering with the QnABot on AWS solution powered by Amazon Lex with Amazon Kendra and large language models

AWS Machine Learning

QnABot allows you to quickly deploy self-service conversational AI into your contact center, websites, and social media channels, reducing costs, shortening hold times, and improving customer experience and brand sentiment. Generated answers can be modified to create the best experience for the intended channel.

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Top 5 Customer Service Articles of the Week 1-16-2023

ShepHyken

Each week, I read many customer service and customer experience articles from various resources. For three decades, the ACSI has been a leading satisfaction index (cause-and-effect metric) connected to the quality of brands sold by companies with significant market share in the United States. I thought it already was!)

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Build production-ready generative AI applications for enterprise search using Haystack pipelines and Amazon SageMaker JumpStart with LLMs

AWS Machine Learning

Enterprise search covers storing documents such as digital files, indexing the documents for search, and providing relevant results based on user queries. This context is embedded into a prompt that is designed to instruct an LLM to generate an answer only from the provided context.

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Harnessing the power of enterprise data with generative AI: Insights from Amazon Kendra, LangChain, and large language models

AWS Machine Learning

However, it’s possible to cross-reference a model answer with the original specialized content, thereby avoiding the need to train a new LLM model, using Retrieval-Augmented Generation (RAG). This integration provides precise and context-aware answers to complex queries by drawing from a diverse range of sources.

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Automate caption creation and search for images at enterprise scale using generative AI and Amazon Kendra

AWS Machine Learning

Amazon Kendra is an intelligent search service powered by machine learning (ML). The Amazon Kendra index can then be enriched with the generated metadata during document ingestion to enable searching the images without any manual effort. Generative AI (GenAI) can be helpful in generating the metadata automatically.

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Question answering using Retrieval Augmented Generation with foundation models in Amazon SageMaker JumpStart

AWS Machine Learning

Today, we announce the availability of sample notebooks that demonstrate question answering tasks using a Retrieval Augmented Generation (RAG)-based approach with large language models (LLMs) in Amazon SageMaker JumpStart. The correct answer should be all SageMaker instances support Managed Spot Training.