NVIDIA

NOVEMBER 15, 2023

Like a good judge, large language models ( LLMs ) can respond to a wide variety of human queries. The court clerk of AI is a process called retrieval-augmented generation, or RAG for short. In other words, it fills a gap in how LLMs work. Another great advantage of RAG is it’s relatively easy. That builds trust.

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AWS Machine Learning Blog

APRIL 8, 2024

Unlocking accurate and insightful answers from vast amounts of text is an exciting capability enabled by large language models (LLMs). When building LLM applications, it is often necessary to connect and query external data sources to provide relevant context to the model.

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AWS Machine Learning Blog

DECEMBER 14, 2023

Retrieval Augmented Generation (RAG) is a process in which a language model retrieves contextual documents from an external data source and uses this information to generate more accurate and informative text. In this post, we demonstrate how to harness the power of RAG to enhance the prompts sent to your Stable Diffusion models.

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Natural Language Processing LLM Prompt Engineer Prompt Engineering 94

Snorkel AI

JANUARY 30, 2024

I recently discussed some of my work on generative AI (GenAI) applications in a talk called “Data Development for GenAI: A Systems Level View” at Snorkel AI’s Enterprise LLM Summit. LLM application ecosystems LLMs don’t exist in a vacuum. The post-processing stage refines the response.

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Snorkel AI

JANUARY 30, 2024

I recently discussed some of my work on generative AI (GenAI) applications in a talk called “Data Development for GenAI: A Systems Level View” at Snorkel AI’s Enterprise LLM Summit. LLM application ecosystems LLMs don’t exist in a vacuum. The post-processing stage refines the response.

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Snorkel AI

SEPTEMBER 20, 2023

Corporate leaders soon urged data science teams to use large language models (LLMs), and data science teams turned to fine-tuning and retrieval-augmented generation (RAG) to mitigate generative AI (genAI) shortcomings. Neither fine-tuning nor RAG requires excluding the other, and the two approaches work better together.

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ODSC - Open Data Science

FEBRUARY 6, 2024

Check out some of the LLM-focused training sessions, workshops, and talks you’ll find at the conference. NLP with GPT-4 and other LLMs: From Training to Deployment with Hugging Face and PyTorch Lightning Dr. Jon Krohn | Chief Data Scientist | Nebula.io

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AWS Machine Learning Blog

JANUARY 26, 2024

Generative artificial intelligence (AI) applications built around large language models (LLMs) have demonstrated the potential to create and accelerate economic value for businesses. This post provides three guided steps to architect risk management strategies while developing generative AI applications using LLMs.

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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. Furthermore, we deep dive on the most common generative AI use case of text-to-text applications and LLM operations (LLMOps), a subset of FMOps.

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ODSC - Open Data Science

MARCH 8, 2024

An implementation with CLIP and GPT-4-vision In recent years, Large Language Models (LLMs) have demonstrated remarkable capabilities across various natural language understanding and generation tasks. Here are some key challenges faced by LLMs: Outdated Knowledge : LLMs lack real-time updates and may provide outdated information.

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Towards AI

APRIL 1, 2024

A few days ago, Databricks open sourced DBRX, a massive general-purpose LLM that show incredible performance across different benchmarks. Databricks released both the baseline model DBRX Base as well as the intstruction fine-tuned one DBRX Instruct.

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AWS Machine Learning Blog

MAY 2, 2023

Today, we announce the availability of sample notebooks that demonstrate question answering tasks using a Retrieval Augmented Generation (RAG)-based approach with large language models (LLMs) in Amazon SageMaker JumpStart. LLMs can store factual knowledge by training their parameters on a large corpus of natural language data.

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AWS Machine Learning Blog

NOVEMBER 7, 2023

Furthermore, the cost to train new LLMs can prove prohibitive for many enterprise settings. However, it’s possible to cross-reference a model answer with the original specialized content, thereby avoiding the need to train a new LLM model, using Retrieval-Augmented Generation (RAG).

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Large Language Models Generative AI LLM AI 94

Snorkel AI

FEBRUARY 6, 2024

Alex Ratner spoke with Alex Taylor, Global Head of Emerging Technology at QBE Ventures at SnorkelAI’s Enterprise LLM Summit in January 2024. The pair explored the challenges and impact of AI in the enterprise—particularly in the insurance world—and the importance of amplifying subject matter experts in the loop.

Data Science 52

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Snorkel AI

FEBRUARY 6, 2024

Alex Ratner spoke with Alex Taylor, Global Head of Emerging Technology at QBE Ventures at SnorkelAI’s Enterprise LLM Summit in January 2024. The pair explored the challenges and impact of AI in the enterprise—particularly in the insurance world—and the importance of amplifying subject matter experts in the loop.

Data Science 52

Data Science LLM Machine Learning Data Scientist 52

AWS Machine Learning Blog

NOVEMBER 27, 2023

Data preparation is important at multiple stages in Retrieval Augmented Generation ( RAG ) models. Further, we show how to preprocess a dataset for RAG. Specifically, we clean the data and create RAG artifacts to answer the questions about the content of the dataset. Clean data is important for good model performance.

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Topbots

OCTOBER 10, 2023

Source: rawpixel.com Conversational AI is an application of LLMs that has triggered a lot of buzz and attention due to its scalability across many industries and use cases. While conversational systems have existed for decades, LLMs have brought the quality push that was needed for their large-scale adoption.

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AWS Machine Learning Blog

APRIL 3, 2024

Search engines and recommendation systems powered by generative AI can improve the product search experience exponentially by understanding natural language queries and returning more accurate results. The model uses correlations across modalities to improve performance on these tasks compared to individual modal embeddings.

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Unite.AI

MARCH 11, 2024

As we navigate the recent artificial intelligence (AI) developments, a subtle but significant transition is underway, moving from the reliance on standalone AI models like large language models (LLMs) to the more nuanced and collaborative compound AI systems like AlphaGeometry and Retrieval Augmented Generation (RAG) system.

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Unite.AI

APRIL 15, 2024

As the field continues to evolve, new techniques and methodologies are being developed to enhance the performance of retrieval systems, particularly in the context of Retrieval Augmented Generation (RAG). Augmentation : The retrieved information is combined with the user's original query or instruction.

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Flipboard

NOVEMBER 22, 2023

In addition, customers are looking for choices to select the most performant and cost-effective machine learning (ML) model and the ability to perform necessary customization (fine-tuning) to fit their business use cases. The LLM generated text, and the IR system retrieves relevant information from a knowledge base.

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AWS Machine Learning Blog

MAY 14, 2024

These models are pre-trained on massive datasets and, to sometimes fine-tuned with smaller sets of more task specific data. RLHF is a technique that combines rewards and comparisons, with human feedback to pre-train or fine-tune a machine learning (ML) model.

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AWS Machine Learning Blog

MAY 13, 2024

In part 1 of this blog series, we discussed how a large language model (LLM) available on Amazon SageMaker JumpStart can be fine-tuned for the task of radiology report impression generation. When summarizing healthcare texts, pre-trained LLMs do not always achieve optimal performance.

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Generative AI Prompt Engineer Prompt Engineering LLM 105

Unite.AI

AUGUST 1, 2023

Prompts serve as the primary means of controlling the output generated by these models, making them crucial for successful and relevant conversations. Poorly-defined prompts can result in output that is not helpful to the user and may even lead to misleading content. Prompt 1 : “Tell me about Convolutional Neural Networks.”

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Prompt Engineer Prompt Engineering ChatGPT Convolutional Neural Networks 246

AWS Machine Learning Blog

MARCH 28, 2024

With the advancements being made with LLMs like the Mixtral-8x7B Instruct , derivative of architectures such as the mixture of experts (MoE) , customers are continuously looking for ways to improve the performance and accuracy of generative AI applications while allowing them to effectively use a wider range of closed and open source models.

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AWS Machine Learning Blog

MAY 9, 2024

This post is co-written with Tom Famularo, Abhay Shah and Nicolette Kontor from Verisk. Verisk (Nasdaq: VRSK) is a leading data analytics and technology partner for the global insurance industry. The company is committed to ethical and responsible AI development, with human oversight and transparency.

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AWS Machine Learning Blog

DECEMBER 6, 2023

Conversational AI has come a long way in recent years thanks to the rapid developments in generative AI, especially the performance improvements of large language models (LLMs) introduced by training techniques such as instruction fine-tuning and reinforcement learning from human feedback.

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Metadata LLM NLP Conversational AI 101

AWS Machine Learning Blog

SEPTEMBER 27, 2023

The existing generative AI solutions for question answering are mainly based on Retrieval Augmented Generation (RAG). In this case, the question answering task works better by fine-tuning the LLM with the documents. To address these challenges, we present a new framework with RAG and fine-tuned LLMs.

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AWS Machine Learning Blog

JANUARY 4, 2024

We are seeing numerous uses, including text generation, code generation, summarization, translation, chatbots, and more. One such area that is evolving is using natural language processing (NLP) to unlock new opportunities for accessing data through intuitive SQL queries. What percentage of customers are from each region?”

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Prompt Engineer Prompt Engineering Generative AI LLM 119

Flipboard

DECEMBER 6, 2023

Enterprises turn to Retrieval Augmented Generation (RAG) as a mainstream approach to building Q&A chatbots. Finally, we take a closer look at how the Q4 team used Amazon Bedrock and SQLDatabaseChain to implement a RAG-based solution with SQL generation. This post is co-written with Stanislav Yeshchenko from Q4 Inc.

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Chatbots LLM Prompt Engineer Prompt Engineering 130

AWS Machine Learning Blog

SEPTEMBER 21, 2023

In particular, we provide practical best practices for different customization scenarios, including training models from scratch, fine-tuning with additional data using full or parameter-efficient techniques, Retrieval Augmented Generation (RAG), and prompt engineering.

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Generative AI Prompt Engineer Prompt Engineering Deep Learning 100

AWS Machine Learning Blog

OCTOBER 24, 2023

In this post, we discuss how to extend a new or existing IDP architecture with large language models (LLMs). You can use LLMs in one or all phases of IDP depending on the use case and desired outcome. In this architecture, LLMs are used to perform specific tasks within the IDP workflow. However, the potential doesn’t end there.

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IDP LLM Prompt Engineer Prompt Engineering 113

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. Retrieval Augmented Generation (RAG) enables LLMs to extract and synthesize information like an advanced search engine.

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Flipboard

NOVEMBER 20, 2023

Retrieval Augmented Generation (RAG) allows you to provide a large language model (LLM) with access to data from external knowledge sources such as repositories, databases, and APIs without the need to fine-tune it. In this post, we demonstrate how to use Amazon SageMaker Studio to build a RAG question answering solution.

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Auto-complete LLM Machine Learning Natural Language Processing 128

deepsense.ai

APRIL 25, 2024

Our latest achievement combines Advanced Retrieval-Augmented Generation (RAG) with Small Language Models (SLMs), aiming to enhance the capabilities of embedded devices beyond traditional cloud solutions. Moreover, companies seeking to cut LLM costs can benefit from shifting inference to local PC hardware.

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Prompt Engineer Prompt Engineering Inference Engine LLM 52

Heartbeat

DECEMBER 15, 2023

And an Assessment of How They Impact Your ragas Metrics Photo by Ilya Pavlov on Unsplash If you’ve ever wondered how the quality of information sourced by language models affects their outputs, you’re in the right place. I’m trying to unpack how different document loaders in LangChain impact a Retrieval Augmented Generation (RAG) system.

AI Chatbots 52

AI Chatbots LLM Chatbots NLP 52

AWS Machine Learning Blog

NOVEMBER 15, 2023

Llama 2 demonstrates the potential of large language models (LLMs) through its refined abilities and precisely tuned performance. Diving deeper into Llama 2’s architecture, Meta reveals that the model’s fine-tuning melds supervised fine-tuning (SFT) with reinforcement learning aided by human feedback (RLHF).

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