Remove Chatbots Remove Metadata Remove Natural Language Processing
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How to use foundation models and trusted governance to manage AI workflow risk

IBM Journey to AI blog

It includes processes that trace and document the origin of data, models and associated metadata and pipelines for audits. Most of today’s largest foundation models, including the large language model (LLM) powering ChatGPT, have been trained on information culled from the internet. Trustworthiness is critical.

Metadata 220
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Unleashing the multimodal power of Amazon Bedrock Data Automation to transform unstructured data into actionable insights

AWS Machine Learning Blog

The processed data is stored in Amazon Bedrock Knowledge Bases, where an embedding model converts it into vector representations, which are then stored in a vector database for efficient semantic search. This architecture enhances automated data processing, efficient retrieval, and seamless real-time access to insights.

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Building AI chatbots using Amazon Lex and Amazon Kendra for filtering query results based on user context

AWS Machine Learning Blog

Solution overview To solve this problem, you can identify one or more unique metadata information that is associated with the documents being indexed and searched. When the user signs in to an Amazon Lex chatbot, user context information can be derived from Amazon Cognito.

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How Q4 Inc. used Amazon Bedrock, RAG, and SQLDatabaseChain to address numerical and structured dataset challenges building their Q&A chatbot

Flipboard

Enterprises turn to Retrieval Augmented Generation (RAG) as a mainstream approach to building Q&A chatbots. We continue to see emerging challenges stemming from the nature of the assortment of datasets available. Application integration The Q&A chatbot capability is one of Q4’s AI services.

Chatbots 168
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Transforming financial analysis with CreditAI on Amazon Bedrock: Octus’s journey with AWS

AWS Machine Learning Blog

Using advanced GenAI, CreditAI by Octus is a flagship conversational chatbot that supports natural language queries and real-time data access with source attribution, significantly reducing analysis time and streamlining research workflows. Amazon Textract processes the documents to extract both text and structural information.

DevOps 81
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Top Large Language Models LLMs Courses

Marktechpost

Prompt Engineering with LLaMA-2 Difficulty Level: Beginner This course covers the prompt engineering techniques that enhance the capabilities of large language models (LLMs) like LLaMA-2. Students will learn to write precise prompts, edit system messages, and incorporate prompt-response history to create AI assistant and chatbot behavior.

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The most valuable AI use cases for business

IBM Journey to AI blog

Voice-based queries use natural language processing (NLP) and sentiment analysis for speech recognition so their conversations can begin immediately. The use of chatbots in remote healthcare appointments requires less human intervention and often a shorter time to diagnosis.