Marktechpost

APRIL 16, 2024

LangChain is an open-source framework that allows developers to build LLM-based applications easily. It provides for easily connecting LLMs with external data sources to augment the capabilities of these models and achieve better results. It teaches how to build LLM-powered applications using LangChain using hands-on exercises.

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Unite.AI

AUGUST 28, 2023

Current Landscape of AI Agents AI agents, including Auto-GPT, AgentGPT, and BabyAGI, are heralding a new era in the expansive AI universe. AI Agents vs. ChatGPT Many advanced AI agents, such as Auto-GPT and BabyAGI, utilize the GPT architecture. Their primary focus is to minimize the need for human intervention in AI task completion.

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AWS Machine Learning Blog

MAY 17, 2024

A prompt is the information you pass into an LLM to elicit a response. The process of designing and refining prompts to get specific responses from these models is called prompt engineering. With prompt chaining, you construct a set of smaller subtasks as individual prompts.

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Unite.AI

SEPTEMBER 11, 2023

Last time we delved into AutoGPT and GPT-Engineering , the early mainstream open-source LLM-based AI agents designed to automate complex tasks. Enter MetaGPT — a Multi-agent system that utilizes Large Language models by Sirui Hong fuses Standardized Operating Procedures (SOPs) with LLM-based multi-agent systems.

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Flipboard

JUNE 13, 2023

Redfin Photo) “We’ve already found a number of places where AI tools are making our engineers more efficient. The auto-complete and auto-suggestions in Visual Studio Code are pretty good, too, without being annoying. ” We’ve also found ways to use these tools to help us serve customers more efficiently.

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AWS Machine Learning Blog

AUGUST 22, 2024

The following figure shows the Discovery Navigator generative AI auto-summary pipeline. Verisk’s evaluation involved three major parts: Prompt engineering – Prompt engineering is the process where you guide generative AI solutions to generate desired output.

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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. Prompt-response management: Refining LLM-backed applications through continuous prompt-response optimization and quality control.

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AWS Machine Learning Blog

JUNE 20, 2024

It allows LLMs to reference authoritative knowledge bases or internal repositories before generating responses, producing output tailored to specific domains or contexts while providing relevance, accuracy, and efficiency. Generation is the process of generating the final response from the LLM.

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Auto-classification LLM Prompt Engineer Prompt Engineering 132

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())

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AWS Machine Learning Blog

SEPTEMBER 14, 2023

To improve the quality of output, approaches like n-short learning, Prompt engineering, Retrieval Augmented Generation (RAG) and fine tuning are used. In addition to QLoRA, bitsanbytes is used to convert to 4-bit precision to quantize frozen LLM to 4-bit and attach LoRA adapters on it.

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Snorkel AI

JUNE 7, 2023

Foundation models, Alex said, yield results that are “nothing short of breathtaking,” but they’re not a complete answer for enterprises who aim to solve challenges using machine learning. “We We are, in our view, in a bit of a hype cycle,” he said.

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Snorkel AI

JUNE 7, 2023

Foundation models, Alex said, yield results that are “nothing short of breathtaking,” but they’re not a complete answer for enterprises who aim to solve challenges using machine learning. “We We are, in our view, in a bit of a hype cycle,” he said.

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Snorkel AI

MARCH 2, 2023

Ali Arsanjani, director of cloud partner engineering at Google Cloud , presented a talk entitled “Challenges and Ethics of DLM and LLM Adoption in the Enterprise” at Snorkel AI’s recent Foundation Model Virtual Summit. Others, toward language completion and further downstream tasks.

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Snorkel AI

MARCH 2, 2023

Ali Arsanjani, director of cloud partner engineering at Google Cloud , presented a talk entitled “Challenges and Ethics of DLM and LLM Adoption in the Enterprise” at Snorkel AI’s recent Foundation Model Virtual Summit. Others, toward language completion and further downstream tasks.

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AWS Machine Learning Blog

APRIL 16, 2024

To store information in Secrets Manager, complete the following steps: On the Secrets Manager console, choose Store a new secret. Complete the following steps: On the Secrets Manager console, choose Store a new secret. This adaptation is facilitated through the use of LLM prompts.

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The MLOps Blog

JUNE 27, 2023

The platform also offers features for hyperparameter optimization, automating model training workflows, model management, prompt engineering, and no-code ML app development. Can you see the complete model lineage with data/models/experiments used downstream? Is it fast and reliable enough for your workflow?

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Unite.AI

SEPTEMBER 4, 2023

However, the world of LLMs isn't simply a plug-and-play paradise; there are challenges in usability, safety, and computational demands. In this article, we will dive deep into the capabilities of Llama 2 , while providing a detailed walkthrough for setting up this high-performing LLM via Hugging Face and T4 GPUs on Google Colab.

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AWS Machine Learning Blog

NOVEMBER 1, 2023

Combined with large language models (LLM) and Contrastive Language-Image Pre-Training (CLIP) trained with a large quantity of multimodality data, visual language models (VLMs) are particularly adept at tasks like image captioning, object detection and segmentation, and visual question answering.

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Topbots

JUNE 29, 2023

On a more advanced stance, everyone who has done SQL query optimisation will know that many roads lead to the same result, and semantically equivalent queries might have completely different syntax. 3] provides a more complete survey of Text2SQL data augmentation techniques.

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