Data Integration and Prompt Engineering - Artificial Intelligence Zone

Unlocking value: Top digital transformation trends

IBM Journey to AI blog

JANUARY 15, 2024

For example, generative AI as a prompt engine will improve efficiency by dramatically reducing the time humans take to create outlines, come up with ideas and learn important information. This approach involves moving cybersecurity considerations to the beginning of the development cycle, embedding them more directly in the code.

Machine Learning

Machine Learning Automation DevOps Artificial Intelligence

Evaluate RAG responses with Amazon Bedrock, LlamaIndex and RAGAS

AWS Machine Learning Blog

MARCH 6, 2025

It simplifies data integration from various sources and provides tools for data indexing, engines, agents, and application integrations. You also define a prompt template following Claude prompt engineering guidelines. LlamaIndex is a framework for building LLM applications.

LLM

LLM Generative AI ML Prompt Engineer

How SnapLogic built a text-to-pipeline application with Amazon Bedrock to translate business intent into action

Flipboard

NOVEMBER 24, 2023

Iris was designed to use machine learning (ML) algorithms to predict the next steps in building a data pipeline. By analyzing millions of metadata elements and data flows, Iris could make intelligent suggestions to users, democratizing data integration and allowing even those without a deep technical background to create complex workflows.

ETL

ETL Prompt Engineer Prompt Engineering Generative AI

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Automation, Evolved: Your New Playbook For Smarter Knowledge Work

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Enrich your AWS Glue Data Catalog with generative AI metadata using Amazon Bedrock

Flipboard

NOVEMBER 15, 2024

This post shows you how to enrich your AWS Glue Data Catalog with dynamic metadata using foundation models (FMs) on Amazon Bedrock and your data documentation. AWS Glue is a serverless data integration service that makes it straightforward for analytics users to discover, prepare, move, and integrate data from multiple sources.

Metadata

Metadata Generative AI LLM AI

Evaluating RAG applications with Amazon Bedrock knowledge base evaluation

AWS Machine Learning Blog

MARCH 14, 2025

The workflow is as follows, as shown moving from left to right in the following architecture diagram: Prompt dataset Prepared set of prompts, optionally including ground truth responses JSONL file Prompt dataset converted to JSONL format for the evaluation job Amazon Simple Storage Service (Amazon S3) bucket Storage for the prepared JSONL file Amazon (..)

Generative AI

Generative AI Responsible AI Automation LLM

The executive’s guide to generative AI for sustainability

AWS Machine Learning Blog

APRIL 22, 2024

Figure 5 offers an overview on generative AI modalities and optimization strategies, including prompt engineering , Retrieval Augmented Generation , and fine-tuning or continued pre-training.

ESG

ESG Generative AI AI AI

Unlocking generative AI for enterprises: How SnapLogic powers their low-code Agent Creator using Amazon Bedrock

AWS Machine Learning Blog

OCTOBER 23, 2024

Seamless customization and integration –The serverless architecture of Amazon Bedrock frees up the time of Agent Creator developers so they can focus on innovation and rapid development. SnapLogic uses Amazon Bedrock to build its platform, capitalizing on the proximity to data already stored in Amazon Web Services (AWS).

Generative AI

Generative AI IDP LLM Automation

Top 10 Data and Analytics Trends for 2024

TransOrg Analytics

OCTOBER 3, 2024

Synthetic data, generated by algorithms rather than collected from real-world events, allows organizations to train AI models and perform data analytics without compromising privacy. This trend will revolutionize industries like healthcare and finance, where data privacy is paramount.

Prompt Engineering

Prompt Engineering Prompt Engineer Data Integration Machine Learning

Top 10 Data and Analytics Trends for 2024

TransOrg Analytics

OCTOBER 3, 2024

Synthetic data, generated by algorithms rather than collected from real-world events, allows organizations to train AI models and perform data analytics without compromising privacy. This trend will revolutionize industries like healthcare and finance, where data privacy is paramount.

Prompt Engineering

Prompt Engineering Prompt Engineer Data Integration Machine Learning

This AI Report from the Illinois Institute of Technology Presents Opportunities and Challenges of Combating Misinformation with LLMs

Marktechpost

JANUARY 27, 2024

Reducing Hallucinations: Fact-Checking and Grounding in Real-World Data: Integrating fact-checking algorithms and knowledge bases into the LLM generation process can help ensure that the outputs are consistent with real-world facts and evidence.

LLM

LLM Large Language Models Explainability Prompt Engineer

AutoAgent: A Fully-Automated and Highly Self-Developing Framework that Enables Users to Create and Deploy LLM Agents through Natural Language Alone

Marktechpost

MARCH 7, 2025

Despite well-documented tools, creating an AI agent usually requires sophisticated prompt engineering, API integration, and debugging, which makes it out of reach for a wider audience. The Self-Managing File System allows seamless data integration, ensuring AI agents can efficiently retrieve and process information.

Automation

Automation LLM AI Automation Software Engineer

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

AWS Machine Learning Blog

MAY 1, 2024

It allows you to retrieve data from sources beyond the foundation model, enhancing prompts by integrating contextually relevant retrieved data. You can use prompt engineering to prevent hallucination and make sure that the answer is grounded in the source documentations.

Chatbots

Chatbots Automation Machine Learning DevOps

Scaling Language Models with LLMOps in Real-World Applications

Heartbeat

MARCH 19, 2024

Like machine learning operations, LLMOps involves efforts from several contributors, like prompt engineers, data scientists, DevOps engineers, business analysts, and IT operations. This is, in fact, a baseline, and the actual LLMOps workflow usually involves more stakeholders like prompt engineers, researchers, etc.

Large Language Models

Large Language Models Prompt Engineering Prompt Engineer Natural Language Processing

MLOps Landscape in 2023: Top Tools and Platforms

The MLOps Blog

JUNE 27, 2023

W&B (Weights & Biases) W&B is a machine learning platform for your data science teams to track experiments, version and iterate on datasets, evaluate model performance, reproduce models, visualize results, spot regressions, and share findings with colleagues. Easy collaboration, annotator management, and QA workflows.

Machine Learning

Machine Learning Metadata Data Scientist Data Quality

Generative AI for agriculture: How Agmatix is improving agriculture with Amazon Bedrock

AWS Machine Learning Blog

NOVEMBER 12, 2024

The response is displayed to the user through the widgets visualizing the trial data and the answer to the user’s specific question, as shown in the following screenshot. The data used in the prompt engineering (trial result and rules) is stored in plain text and sent to the model as is.

Generative AI

Generative AI Prompt Engineer Prompt Engineering AI

How Tealium built a chatbot evaluation platform with Ragas and Auto-Instruct using AWS generative AI services

AWS Machine Learning Blog

DECEMBER 11, 2024

Generating improved instructions for each question-and-answer pair using an automatic prompt engineering technique based on the Auto-Instruct Repository. Tealium background and use case Tealium is a leader in real-time customer data integration and management. This was achieved by building an: Evaluation pipeline.

Generative AI

Generative AI Chatbots Prompt Engineering Prompt Engineer

Use Amazon Bedrock Agents for code scanning, optimization, and remediation

AWS Machine Learning Blog

NOVEMBER 27, 2024

With the Amazon Bedrock serverless experience, you can get started quickly, privately customize FMs with your own data, integrate and deploy them into your application using Amazon Web Services (AWS) tools without having to manage any infrastructure.

Generative AI

Generative AI Automation DevOps Prompt Engineer

Artificial Intelligence Zone

Unlocking value: Top digital transformation trends

Evaluate RAG responses with Amazon Bedrock, LlamaIndex and RAGAS

Webinars

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How SnapLogic built a text-to-pipeline application with Amazon Bedrock to translate business intent into action

Webinars

Enrich your AWS Glue Data Catalog with generative AI metadata using Amazon Bedrock

Evaluating RAG applications with Amazon Bedrock knowledge base evaluation

The executive’s guide to generative AI for sustainability

Unlocking generative AI for enterprises: How SnapLogic powers their low-code Agent Creator using Amazon Bedrock

Top 10 Data and Analytics Trends for 2024

Top 10 Data and Analytics Trends for 2024

This AI Report from the Illinois Institute of Technology Presents Opportunities and Challenges of Combating Misinformation with LLMs

AutoAgent: A Fully-Automated and Highly Self-Developing Framework that Enables Users to Create and Deploy LLM Agents through Natural Language Alone

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

Scaling Language Models with LLMOps in Real-World Applications

MLOps Landscape in 2023: Top Tools and Platforms

Generative AI for agriculture: How Agmatix is improving agriculture with Amazon Bedrock

How Tealium built a chatbot evaluation platform with Ragas and Auto-Instruct using AWS generative AI services

Use Amazon Bedrock Agents for code scanning, optimization, and remediation

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