AWS Machine Learning Blog

JULY 3, 2024

Generative AI and transformer-based large language models (LLMs) have been in the top headlines recently. These models demonstrate impressive performance in question answering, text summarization, code, and text generation. We use prompt engineering to send our summarization instructions to the LLM. Mesko, B., &

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AWS Machine Learning Blog

OCTOBER 6, 2023

Another essential component is an orchestration tool suitable for prompt engineering and managing different type of subtasks. Generative AI developers can use frameworks like LangChain , which offers modules for integrating with LLMs and orchestration tools for task management and prompt engineering.

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Marktechpost

JULY 25, 2024

Introduction to Large Language Models Difficulty Level: Beginner This course covers large language models (LLMs), their use cases, and how to enhance their performance with prompt tuning. This short course also includes guidance on using Google tools to develop your own Generative AI apps.

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AWS Machine Learning Blog

SEPTEMBER 6, 2024

In this post, we illustrate how Vidmob , a creative data company, worked with the AWS Generative AI Innovation Center (GenAIIC) team to uncover meaningful insights at scale within creative data using Amazon Bedrock. Use case overview Vidmob aims to revolutionize its analytics landscape with generative AI.

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AWS Machine Learning Blog

AUGUST 26, 2024

Prospecting, opportunity progression, and customer engagement present exciting opportunities to utilize generative AI, using historical data, to drive efficiency and effectiveness. Use case overview Using generative AI, we built Account Summaries by seamlessly integrating both structured and unstructured data from diverse sources.

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AWS Machine Learning Blog

SEPTEMBER 1, 2023

Nowadays, the majority of our customers is excited about large language models (LLMs) and thinking how generative AI could transform their business. In this post, we discuss how to operationalize generative AI applications using MLOps principles leading to foundation model operations (FMOps).

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AWS Machine Learning Blog

JUNE 13, 2023

Organizations can maximize the value of their modern data architecture with generative AI solutions while innovating continuously. The latest offering for generative AI from AWS is Amazon Bedrock , which is a fully managed service and the easiest way to build and scale generative AI applications with foundation models.

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AWS Machine Learning Blog

AUGUST 8, 2024

As one of the largest AWS customers, Twilio engages with data, artificial intelligence (AI), and machine learning (ML) services to run their daily workloads. Data is the foundational layer for all generative AI and ML applications. Create a simple web application using LangChain and Streamlit.

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Metadata LLM Prompt Engineer Prompt Engineering 114

Marktechpost

MAY 30, 2024

Inspect Rich Documents with Gemini Multimodality and Multimodal RAG This course covers using multimodal prompts to extract information from text and visual data and generate video descriptions with Gemini. It also includes guidance on using Google Tools to develop your own Generative AI applications.

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Unite.AI

OCTOBER 7, 2024

Yes, they have the data, the metadata, the workflows and a vast array of services to connect into; and so long as your systems only live within Salesforce, it sounds pretty ideal. Whether that UI is rendered using generative AI or some other non-AI mechanism is left to the responding service as an implementation detail.

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AWS Machine Learning Blog

AUGUST 9, 2024

Retrieval Augmented Generation (RAG) has emerged as a leading method for using the power of large language models (LLMs) to interact with documents in natural language. The text embedding model processes the text chunks and generates embedding vectors for each text chunk. The second step is Q&A, as shown in the following diagram.

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AWS Machine Learning Blog

JUNE 25, 2024

With Amazon Bedrock, developers can experiment, evaluate, and deploy generative AI applications without worrying about infrastructure management. Its enterprise-grade security, privacy controls, and responsible AI features enable secure and trustworthy generative AI innovation at scale.

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AWS Machine Learning Blog

MAY 7, 2024

Although much of the current excitement is around LLMs for generative AI tasks, many of the key use cases that you might want to solve have not fundamentally changed. This post walks through examples of building information extraction use cases by combining LLMs with prompt engineering and frameworks such as LangChain.

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AWS Machine Learning Blog

MAY 30, 2024

Because Amazon Bedrock is serverless, you don’t have to manage infrastructure, and you can securely integrate and deploy generative AI capabilities into your applications using the AWS services you are already familiar with. The wrapper function reads the table metadata from the S3 bucket. If it finds any, it skips to Step 6.

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Unite.AI

AUGUST 4, 2023

In this article, we will delve deeper into these issues, exploring the advanced techniques of prompt engineering with Langchain, offering clear explanations, practical examples, and step-by-step instructions on how to implement them. Prompts play a crucial role in steering the behavior of a model.

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AWS Machine Learning Blog

AUGUST 21, 2024

This post discusses how LLMs can be accessed through Amazon Bedrock to build a generative AI solution that automatically summarizes key information, recognizes the customer sentiment, and generates actionable insights from customer reviews. Our example prompt requests the FM to generate the response in JSON format.

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IBM Journey to AI blog

JULY 17, 2023

is our enterprise-ready next-generation studio for AI builders, bringing together traditional machine learning (ML) and new generative AI capabilities powered by foundation models. With watsonx.ai, businesses can effectively train, validate, tune and deploy AI models with confidence and at scale across their enterprise.

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AWS Machine Learning Blog

MAY 30, 2024

Additionally, VitechIQ includes metadata from the vector database (for example, document URLs) in the model’s output, providing users with source attribution and enhancing trust in the generated answers. Prompt engineering Prompt engineering is crucial for the knowledge retrieval system.

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Chatbots Prompt Engineer Prompt Engineering Large Language Models 122

John Snow Labs

OCTOBER 16, 2023

As the market for generative AI solutions is poised to hit $51.8 McKinsey & Company’s findings underscore 2023 as a landmark year for generative AI, hinting at the transformative wave ahead. solves this problem by extracting metadata during the data preparation process. Unstructured.IO Unstructured.IO

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ODSC - Open Data Science

OCTOBER 6, 2023

Be sure to check out his talk, “ Prompt Optimization with GPT-4 and Langchain ,” there! The difference between the average person using AI and a Prompt Engineer is testing. Most people run a prompt 2–3 times and find something that works well enough.

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ODSC - Open Data Science

JULY 12, 2024

Will it continue to be LLMs and generative AI or will it be something completely new? AI in Robotics Discover the forefront of AI and robotics, from foundation models to real-world applications.

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Flipboard

DECEMBER 6, 2023

This general behavior can be optimized to a specific domain or industry by further optimizing a foundation model using additional domain-specific pre-training data or by fine-tuning using labeled data. Considering the quality of the SQL generated, we started to look at how much value the validation stage is actually adding.

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AWS Machine Learning Blog

JULY 24, 2024

Large language models (LLMs) have achieved remarkable success in various natural language processing (NLP) tasks, but they may not always generalize well to specific domains or tasks. You can customize the model using prompt engineering, Retrieval Augmented Generation (RAG), or fine-tuning.

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AWS Machine Learning Blog

OCTOBER 24, 2023

By integrating generative artificial intelligence (AI) into the process, we can further enhance IDP capabilities. Generative AI not only introduces enhanced capabilities in document processing, it also introduces a dynamic adaptability to changing data patterns.

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AWS Machine Learning Blog

OCTOBER 24, 2024

The AWS Generative AI Innovation Center (GenAIIC) is a team of AWS science and strategy experts who have deep knowledge of generative AI. They help AWS customers jumpstart their generative AI journey by building proofs of concept that use generative AI to bring business value. doc,pdf, or.txt).

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Heartbeat

FEBRUARY 8, 2024

These building blocks, similar to functions and object classes, are essential components for creating generative AI programs. LangChain loads training data in the form of ‘Documents’, and these contain text from various sources and their respective metadata. The rest are optional like prompt template, metadata, tags, etc.

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Conversational AI Chatbots LLM Prompt Engineer 52

AWS Machine Learning Blog

SEPTEMBER 15, 2023

Often, these LLMs require some metadata about available tools (descriptions, yaml, or JSON schema for their input parameters) in order to output tool invocations. We use prompt engineering only and Flan-UL2 model as-is without fine-tuning. You have access to the following tools.

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AWS Machine Learning Blog

MAY 17, 2023

AWS delivers services that meet customers’ artificial intelligence (AI) and machine learning (ML) needs with services ranging from custom hardware like AWS Trainium and AWS Inferentia to generative AI foundation models (FMs) on Amazon Bedrock. Download the generated text file to view the transcription.

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Mlearning.ai

MARCH 16, 2023

Shutterstock Datasets and AI-generated Content: Contributor FAQ They present this as a responsible and ethical approach to AI-generated content but I think they tend to overestimate the role of the individual training images and underestimate the role of prompt engineering.

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AWS Machine Learning Blog

JUNE 20, 2024

Additionally, evaluation can identify potential biases, hallucinations, inconsistencies, or factual errors that may arise from the integration of external sources or from sub-optimal prompt engineering. In this case, the model choice needs to be revisited or further prompt engineering needs to be done.

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Auto-classification LLM Prompt Engineer Prompt Engineering 125

Snorkel AI

MARCH 2, 2023

I am Ali Arsanjani, and I lead partner engineering for Google Cloud, specializing in the area of AI-ML, and I’m very happy to be here today with everyone. But, in order to actually adapt the foundation model or generative AI process into the enterprise, we need to look at what changes or potential additions we may need to make.

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Snorkel AI

MARCH 2, 2023

I am Ali Arsanjani, and I lead partner engineering for Google Cloud, specializing in the area of AI-ML, and I’m very happy to be here today with everyone. But, in order to actually adapt the foundation model or generative AI process into the enterprise, we need to look at what changes or potential additions we may need to make.

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Large Language Models Prompt Engineer Prompt Engineering Neural Network 52

deepsense.ai

JULY 30, 2023

Model training is only a small part of a typical machine learning project (source: own study) Of course, in the context of Large Language Models, we often talk about just fine tuning, few-shot learning or just prompt engineering instead of a full training procedure. This triggers a bunch of quality checks (e.g.

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AWS Machine Learning Blog

APRIL 16, 2024

To learn more about SageMaker Studio JupyterLab Spaces, refer to Boost productivity on Amazon SageMaker Studio: Introducing JupyterLab Spaces and generative AI tools. Text to SQL: Using natural language to enhance query authoring SQL is a complex language that requires an understanding of databases, tables, syntaxes, and metadata.

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Heartbeat

NOVEMBER 8, 2023

'}] generate is similar to apply, except it returns an LLMResult instead of a string. Use this when you want the entire LLMResult object returned, not just the generated text. This gives you access to metadata like the number of tokens used.

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AWS Machine Learning Blog

NOVEMBER 29, 2023

An evaluation is a task used to measure the quality and responsibility of output of an LLM or generative AI service. Based on this tenet, we can classify generative AI users who need LLM evaluation capabilities into 3 groups as shown in the following figure: model providers, fine-tuners, and consumers.

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AWS Machine Learning Blog

AUGUST 30, 2024

With the rapid growth of generative artificial intelligence (AI), many AWS customers are looking to take advantage of publicly available foundation models (FMs) and technologies. This licensing update reflects Meta’s commitment to fostering innovation and collaboration in AI development with transparency and accountability.

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