Remove Chatbots Remove Data Ingestion Remove Responsible AI
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How Deltek uses Amazon Bedrock for question and answering on government solicitation documents

AWS Machine Learning Blog

Question and answering (Q&A) using documents is a commonly used application in various use cases like customer support chatbots, legal research assistants, and healthcare advisors. The first step is data ingestion, as shown in the following diagram. This structure can be used to optimize data ingestion.

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How Twilio generated SQL using Looker Modeling Language data with Amazon Bedrock

AWS Machine Learning Blog

Content ingestion into vector db Select the optimal LLM for your use case Selecting the right LLM for any use case is essential. Every use case has different requirements for context length, token size, and the ability to handle various tasks like summarization, task completion, chatbot applications, and so on.

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Announcing the First Sessions for ODSC East 2024

ODSC - Open Data Science

These concerns include lack of interpretability, bias, and discrimination, privacy, lack of model robustness, fake and misleading content, copyright implications, plagiarism, and environmental impact associated with training and inference of generative AI models. Sign me up!

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Reducing hallucinations in large language models with custom intervention using Amazon Bedrock Agents

Flipboard

In this post, we will set up our own custom agentic AI workflow using Amazon Bedrock Agents to intervene when LLM hallucinations are detected and route the user query to customer service agents through a human-in-the-loop process. The final agent response is shown in the chatbot UI(User Interface).

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Dive deep into vector data stores using Amazon Bedrock Knowledge Bases

AWS Machine Learning Blog

The following diagram depicts the high-level steps of a RAG process to access an organization’s internal or external knowledge stores and pass the data to the LLM. The workflow consists of the following steps: Either a user through a chatbot UI or an automated process issues a prompt and requests a response from the LLM-based application.

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