AWS Machine Learning Blog

AUGUST 9, 2024

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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AWS Machine Learning Blog

NOVEMBER 26, 2023

You follow the same process of data ingestion, training, and creating a batch inference job as in the previous use case. Getting recommendations along with metadata makes it more convenient to provide additional context to LLMs. You can also use this for sequential chains.

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AWS Machine Learning Blog

APRIL 26, 2024

With Knowledge Bases for Amazon Bedrock, you can securely connect foundation models (FMs) in Amazon Bedrock to your company data for fully managed Retrieval Augmented Generation (RAG). You can now interact with your documents in real time without prior data ingestion or database configuration.

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AWS Machine Learning Blog

FEBRUARY 5, 2025

Amazon Kendra also supports the use of metadata for each source file, which enables both UIs to provide a link to its sources, whether it is the Spack documentation website or a CloudFront link. Furthermore, Amazon Kendra supports relevance tuning , enabling boosting certain data sources.

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AWS Machine Learning Blog

AUGUST 8, 2024

This post highlights how Twilio enabled natural language-driven data exploration of business intelligence (BI) data with RAG and Amazon Bedrock. Twilio’s use case Twilio wanted to provide an AI assistant to help their data analysts find data in their data lake.

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AWS Machine Learning Blog

OCTOBER 6, 2023

The applications also extend into retail, where they can enhance customer experiences through dynamic chatbots and AI assistants, and into digital marketing, where they can organize customer feedback and recommend products based on descriptions and purchase behaviors. A feature store maintains user profile data.

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deepsense.ai

JULY 30, 2023

Other steps include: data ingestion, validation and preprocessing, model deployment and versioning of model artifacts, live monitoring of large language models in a production environment, monitoring the quality of deployed models and potentially retraining them. This triggers a bunch of quality checks (e.g.

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