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AI in Customer Service: Chatbots and ChatGPT are just the Start

TechSee

While initial conversations now focus on improving chatbots with large language models (LLMs) like ChatGPT, this is just the start of what AI can and will offer. Deploying this AI will require more than simply upgrading a chatbot. AI is rapidly becoming a critical tool in customer service.

Chatbots 202
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What Timeframe for an AI Chatbot Project?

Inbenta

Depending on the context in which the chatbot project takes place, and therefore its scope of action, its implementation may take more or less time. Indeed, the development of a chatbot implies creating new jobs such as the one of Botmaster for example. How long does it take to deploy an AI chatbot? Let’s see what these can be.

Chatbots 140
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Streamline workflow orchestration of a system of enterprise APIs using chaining with Amazon Bedrock Agents

AWS Machine Learning

Intricate workflows that require dynamic and complex API orchestration can often be complex to manage. In this post, we explore how chaining domain-specific agents using Amazon Bedrock Agents can transform a system of complex API interactions into streamlined, adaptive workflows, empowering your business to operate with agility and precision.

APIs 133
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Best practices to build generative AI applications on AWS

AWS Machine Learning

By the end, you will have solid guidelines and a helpful flow chart for determining the best method to develop your own FM-powered applications, grounded in real-life examples. Whether creating a chatbot or summarization tool, you can shape powerful FMs to suit your needs.

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Enhancing LLM Capabilities with NeMo Guardrails on Amazon SageMaker JumpStart

AWS Machine Learning

Some links for security best practices are shared below but we strongly recommend reaching out to your account team for detailed guidance and to discuss the appropriate security architecture needed for a secure and compliant deployment. model API exposed by SageMaker JumpStart properly. The Llama 3.1 Heres how we implement this.

Chatbots 112
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Generating value from enterprise data: Best practices for Text2SQL and generative AI

AWS Machine Learning

We are seeing numerous uses, including text generation, code generation, summarization, translation, chatbots, and more. In this post, we provide an introduction to text to SQL (Text2SQL) and explore use cases, challenges, design patterns, and best practices. Generative AI has opened up a lot of potential in the field of AI.

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Automate chatbot for document and data retrieval using Agents and Knowledge Bases for Amazon Bedrock

AWS Machine Learning

Numerous customers face challenges in managing diverse data sources and seek a chatbot solution capable of orchestrating these sources to offer comprehensive answers. This post presents a solution for developing a chatbot capable of answering queries from both documentation and databases, with straightforward deployment.

Chatbots 131