Data Platform LLM Metadata 
NOVEMBER 19, 2024
Solution overview By combining the powerful vector search capabilities of OpenSearch Service with the access control features provided by Amazon Cognito , this solution enables organizations to manage access controls based on custom user attributes and document metadata. If you don’t already have an AWS account, you can create one.
AWS Machine Learning Blog
DECEMBER 5, 2024
Used alongside other techniques such as prompt engineering, RAG, and contextual grounding checks, Automated Reasoning checks add a more rigorous and verifiable approach to enhancing the accuracy of LLM-generated outputs. Amazon Bedrock Evaluations addresses this by helping you evaluate, compare, and select the best FMs for your use case.
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IBM Journey to AI blog
OCTOBER 14, 2024
Just last month, Salesforce made a major acquisition to power its Agentforce platform—just one in a number of recent investments in unstructured data management providers. “Most data being generated every day is unstructured and presents the biggest new opportunity.”
AWS Machine Learning Blog
MARCH 5, 2025
To scale ground truth generation and curation, you can apply a risk-based approach in conjunction with a prompt-based strategy using LLMs. Its important to note that LLM-generated ground truth isnt a substitute for use case SME involvement. To convert the source document excerpt into ground truth, we provide a base LLM prompt template.
AWS Machine Learning Blog
AUGUST 8, 2024
Next, you need to index this data to make it available for a Retrieval Augmented Generation (RAG) approach where relevant passages are delivered with high accuracy to a large language model (LLM). A data source connector is a component of Amazon Q that helps integrate and synchronize data from multiple repositories into one index.
TheSequence
JULY 5, 2023
Created Using Midjourney In case you missed yesterday’s newsletter due to July the 4th holiday, we discussed the universe of in-context retrieval augmented LLMs or techniques that allow to expand the LLM knowledge without altering its core architecutre. It’s a good one. Go check it out.
The MLOps Blog
MARCH 12, 2024
TL;DR LLMOps involves managing the entire lifecycle of Large Language Models (LLMs), including data and prompt management, model fine-tuning and evaluation, pipeline orchestration, and LLM deployment. However, transforming raw LLMs into production-ready applications presents complex challenges. using techniques like RLHF.)
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