Marktechpost

JUNE 15, 2024

Artificial intelligence’s large language models (LLMs) have become essential tools due to their ability to process and generate human-like text, enabling them to perform various tasks. MAGPIE leverages the auto-regressive nature of aligned LLMs to generate high-quality instruction data at scale.

Prompt Engineer 134

Prompt Engineer Prompt Engineering Auto-complete Large Language Models 134

ODSC - Open Data Science

AUGUST 24, 2023

Evolving Trends in Prompt Engineering for Large Language Models (LLMs) with Built-in Responsible AI Practices Editor’s note: Jayachandran Ramachandran and Rohit Sroch are speakers for ODSC APAC this August 22–23. Auto Eval Common Metric Eval Human Eval Custom Model Eval 3.

Prompt Engineering 52

Prompt Engineering Prompt Engineer Large Language Models Responsible AI 52

AWS Machine Learning Blog

MAY 17, 2023

The performance and quality of the models also improved drastically with the number of parameters. These models span tasks like text-to-text, text-to-image, text-to-embedding, and more. You can use large language models (LLMs), more specifically, for tasks including summarization, metadata extraction, and question answering.

Large Language Models 99

Large Language Models Auto-complete ML Generative AI 99

Towards AI

APRIL 7, 2024

Source : Image generated by author using Yarnit It is quite astonishing how Large Language Models or LLMs (GPT, Claude, Gemini etc.) It’s a powerful technology that can tackle a variety of natural language tasks. In their paper, “Chain-of-Thought Prompting Elicits Reasoning in Large Language Models”, Wei et.

LLM 104

LLM Auto-complete Prompt Engineer Prompt Engineering 104

Marktechpost

MARCH 6, 2025

Running large language models (LLMs) presents significant challenges due to their hardware demands, but numerous options exist to make these powerful tools accessible. Plug in the coffee maker and press the POWER button. Press the BREW button to start brewing.

Large Language Models 54

Large Language Models Auto-complete Prompt Engineer Prompt Engineering 54

Marktechpost

APRIL 16, 2024

Quick Start Guide to Large Language Models This book guides how to work with, integrate, and deploy LLMs to solve real-world problems. The book covers the inner workings of LLMs and provides sample codes for working with models like GPT-4, BERT, T5, LLaMA, etc.

Large Language Models 104

Large Language Models Auto-complete Prompt Engineer Prompt Engineering 104

Flipboard

JUNE 13, 2023

Large language models are great at this kind of focused, pattern-based code building. This said, it’s important to emphasize that we still need experienced and skilled engineers for 90% of our work. The auto-complete and auto-suggestions in Visual Studio Code are pretty good, too, without being annoying.

Generative AI 181

Generative AI Auto-complete Software Engineer AI 181

AWS Machine Learning Blog

AUGUST 22, 2024

The following figure shows the Discovery Navigator generative AI auto-summary pipeline. The OCR converted medical record pages are processed through Verisk’s AI models and select pages are sent to Amazon Bedrock using AWS PrivateLink , for generating visit summaries.

Generative AI 119

Generative AI Auto-complete Software Development Automation 119

AWS Machine Learning Blog

MAY 17, 2024

They’re capable of performing a wide variety of general tasks with a high degree of accuracy based on input prompts. Large language models (LLMs) are one class of FMs. This includes task context, data that you pass to the model, conversation and action history, instructions, and even examples.

Generative AI 138

Generative AI LLM Prompt Engineer Prompt Engineering 138

AWS Machine Learning Blog

DECEMBER 5, 2023

The insurance provider receives payout claims from the beneficiary’s attorney for different insurance types, such as home, auto, and life insurance. When this is complete, the document can be routed to the appropriate department or downstream process. The following diagram outlines the proposed solution architecture. append(e["Text"].upper())

Metadata 125

Metadata Auto-classification Auto-complete Content Enrichment 125

AWS Machine Learning Blog

APRIL 17, 2023

Quantization and compression can reduce model size and serving cost by reducing the precision of weights or reducing the number of parameters via pruning or distillation. Compilation can optimize the computation graph and fuse operators to reduce memory and compute requirements of a model. For more details, refer to the GitHub repo.

Prompt Engineer 104

Prompt Engineer Prompt Engineering Deep Learning Machine Learning 104

AWS Machine Learning Blog

JUNE 20, 2024

Retrieval Augmented Generation (RAG) is a technique that enhances large language models (LLMs) by incorporating external knowledge sources. A score of 1 means that the generated answer conveys the same meaning as the ground truth answer, whereas a score of 0 suggests that the two answers have completely different meanings.

Auto-classification 138

Auto-classification LLM Prompt Engineer Prompt Engineering 138

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. What is Large Language Model Operations (LLMOps)? What the future of LLMOps looks like.

Prompt Engineer 52

Prompt Engineer Prompt Engineering Large Language Models LLM 52

The MLOps Blog

SEPTEMBER 26, 2024

Hallucinations can be detected by verifying the accuracy and reliability of the model’s responses. Effective mitigation strategies involve enhancing data quality, alignment, information retrieval methods, and prompt engineering. In extreme cases, certain tokens can completely break an LLM. In 2022, when GPT-3.5

LLM 72

LLM Prompt Engineer Prompt Engineering Auto-complete 72

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. Is it fast and reliable enough for your workflow?

Machine Learning 134

Machine Learning Metadata Data Scientist Data Quality 134

Snorkel AI

MARCH 2, 2023

Today we’re going to be talking essentially about how responsible generative-AI-model adoption can happen at the enterprise level, and what are some of the promises and compromises we face. The foundation of large language models started quite some time ago. What are the promises? Billions of parameters.

Large Language Models 52

Large Language Models Prompt Engineer Prompt Engineering Neural Network 52

Snorkel AI

MARCH 2, 2023

Today we’re going to be talking essentially about how responsible generative-AI-model adoption can happen at the enterprise level, and what are some of the promises and compromises we face. The foundation of large language models started quite some time ago. What are the promises? Billions of parameters.

Large Language Models 52

Large Language Models Prompt Engineer Prompt Engineering Neural Network 52

Snorkel AI

JUNE 7, 2023

They focussed largely on the challenges and opportunities in leveraging large language models and foundation models , as well as data-centric AI development approaches. Across thirteen sessions, the speakers commented on the current and future state of artificial intelligence and machine learning.

Data Scientist 52

Data Scientist Large Language Models Machine Learning Data Science 52

Snorkel AI

JUNE 7, 2023

They focussed largely on the challenges and opportunities in leveraging large language models and foundation models , as well as data-centric AI development approaches. Across thirteen sessions, the speakers on the current and future state of artificial intelligence and machine learning. Learn more, live!

Data Scientist 52

Data Scientist Large Language Models Machine Learning Data Science 52

AWS Machine Learning Blog

APRIL 16, 2024

Then we show how you can enhance the in-notebook SQL experience using Text-to-SQL capabilities provided by advanced large language models (LLMs) to write complex SQL queries using natural language text as input. Complete the following steps: On the Secrets Manager console, choose Store a new secret.

Data Scientist 127

Data Scientist Generative AI Machine Learning Auto-complete 127

Unite.AI

JUNE 15, 2023

The company’s AI can learn from internal documents, email, chat and even old support tickets to automatically resolve auto-route tickets correctly, and quickly surface the most relevant institutional knowledge. But for example, GPT two was launched, I believe in 2018, 2019, and open source, and there were other models like T5.

Large Language Models 246

Large Language Models Chatbots Generative AI Auto-complete 246

Unite.AI

SEPTEMBER 4, 2023

Large Language Models (LLMs) capable of complex reasoning tasks have shown promise in specialized domains like programming and creative writing. Developed by Meta with its partnership with Microsoft, this open-source large language model aims to redefine the realms of generative AI and natural language understanding.

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ChatGPT Auto-complete Large Language Models LLM 290

AWS Machine Learning Blog

NOVEMBER 1, 2023

Visual language processing (VLP) is at the forefront of generative AI, driving advancements in multimodal learning that encompasses language intelligence, vision understanding, and processing. Additionally, you benefit from advanced features like auto scaling of inference endpoints, enhanced security, and built-in model monitoring.

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Auto-classification LLM Auto-complete Generative AI 123

Topbots

JUNE 29, 2023

In this article, we will consider the different implementation aspects of Text2SQL and focus on modern approaches with the use of Large Language Models (LLMs), which achieve the best performance as of now (cf. [2]; 3] provides a more complete survey of Text2SQL data augmentation techniques.

Algorithm 52

Algorithm Auto-complete Data Scientist Auto-classification 52

AWS Machine Learning Blog

DECEMBER 11, 2024

Two open-source libraries, Ragas (a library for RAG evaluation) and Auto-Instruct, used Amazon Bedrock to power a framework that evaluates and improves upon RAG. Generating improved instructions for each question-and-answer pair using an automatic prompt engineering technique based on the Auto-Instruct Repository.

Generative AI 81

Generative AI Chatbots Prompt Engineer Prompt Engineering 81

AWS Machine Learning Blog

FEBRUARY 3, 2025

Not only are large language models (LLMs) capable of answering a users question based on the transcript of the file, they are also capable of identifying the timestamp (or timestamps) of the transcript during which the answer was discussed. The process takes approximately 20 minutes to complete.

UX Design 110

UX Design Auto-complete LLM Prompt Engineer 110