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. Why are these elements so important? monitoring and automation).

Large Language Models 76

Large Language Models LLM Machine Learning Automation 76

AWS Machine Learning Blog

JUNE 13, 2023

An AWS Glue crawler is scheduled to run at frequent intervals to extract metadata from databases and create table definitions in the AWS Glue Data Catalog. LangChain, a tool to work with LLMs and prompts, is used in Studio notebooks. LangChain requires an LLM to be defined.

Generative AI 72

Generative AI Metadata LLM Large Language Models 72

Heartbeat

JANUARY 26, 2024

In this experiment, I’ll use Comet LLM to record prompts, responses, and metadata for each memory type for performance optimization purposes. Comet LLM provides additional features such as UI visualization, detailed chain execution logs, automatic tracking with OpenAI chat model, and user feedback analysis. .

Metadata 52

Metadata LLM OpenAI Chatbots 52

Unite.AI

OCTOBER 25, 2023

In-context learning has emerged as an alternative, prioritizing the crafting of inputs and prompts to provide the LLM with the necessary context for generating accurate outputs. But the drawback for this is its reliance on the skill and expertise of the user in prompt engineering.

LLM 304

LLM OpenAI Prompt Engineer Prompt Engineering 304

AWS Machine Learning Blog

SEPTEMBER 1, 2023

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. Strong domain knowledge for tuning, including prompt engineering, is required as well. Only prompt engineering is necessary for better results.

Generative AI 102

Generative AI Prompt Engineer Prompt Engineering AI 102

AWS Machine Learning Blog

DECEMBER 5, 2023

Enterprises may want to add custom metadata like document types (W-2 forms or paystubs), various entity types such as names, organization, and address, in addition to the standard metadata like file type, date created, or size to extend the intelligent search while ingesting the documents.

Metadata 98

Metadata Auto-classification Auto-complete Content Enrichment 98

Heartbeat

AUGUST 25, 2023

Introduction Prompt Engineering is arguably the most critical aspect in harnessing the power of Large Language Models (LLMs) like ChatGPT. However; current prompt engineering workflows are incredibly tedious and cumbersome. Logging prompts and their outputs to .csv First install the package via pip.

Prompt Engineer 52

Prompt Engineer Prompt Engineering Large Language Models Metadata 52

Heartbeat

NOVEMBER 22, 2023

Additionally, one can customize the evaluation prompt for task-specific instructions, ensuring the evaluator scores the output as desired. Comparison evaluators in LangChain help measure two different chains or LLM outputs. metadata : (Optional) Additional metadata to associate with the evaluation.

LLM 52

LLM Explainability Metadata Deep Learning 52

AWS Machine Learning Blog

MAY 7, 2024

This post walks through examples of building information extraction use cases by combining LLMs with prompt engineering and frameworks such as LangChain. We also examine the uplift from fine-tuning an LLM for a specific extractive task.

Prompt Engineer 94

Prompt Engineer Prompt Engineering Large Language Models LLM 94

Bugra Akyildiz

MARCH 16, 2024

Articles Vgel wrote a blog post on the representation engineering, focusing on the control vector in LLMs. If you are interested and want to learn about AI safety and how to customize an already trained LLM, this post goes over couple of different ways of doing so. This is where metadata comes in.

Metadata 59

Metadata LLM Machine Learning Python 59

Heartbeat

NOVEMBER 24, 2023

A Document is a piece of text with associated metadata. Following these steps, you can effectively incorporate retrieval capabilities into your LangChain application and enhance the language model’s performance and contextual understanding.

Deep Learning 52

Deep Learning Metadata OpenAI Data Scientist 52

Unite.AI

AUGUST 4, 2023

This, coupled with the challenges of understanding AI concepts and complex algorithms, contributes to the learning curve associated with developing applications using LLMs. Nevertheless, the integration of LLMs with other tools to form LLM-powered applications could redefine our digital landscape.

Prompt Engineer 245

Prompt Engineer Prompt Engineering Python NLP 245

AWS Machine Learning Blog

SEPTEMBER 15, 2023

Large language model (LLM) agents are programs that extend the capabilities of standalone LLMs with 1) access to external tools (APIs, functions, webhooks, plugins, and so on), and 2) the ability to plan and execute tasks in a self-directed fashion. We conclude the post with items to consider before deploying LLM agents to production.

LLM 101

LLM Prompt Engineer Prompt Engineering Large Language Models 101

Flipboard

DECEMBER 6, 2023

The following are some of the experiments that were conducted by the team, along with the challenges identified and lessons learned: Pre-training – Q4 understood the complexity and challenges that come with pre-training an LLM using its own dataset. The context is finally used to augment the input prompt for a summarization step.

Chatbots 130

Chatbots LLM Prompt Engineer Prompt Engineering 130

AWS Machine Learning Blog

OCTOBER 24, 2023

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. Document classification – In addition to using Amazon Comprehend , you can use an LLM to classify documents using few-shot prompting.

IDP 112

IDP LLM Prompt Engineer Prompt Engineering 112

Heartbeat

SEPTEMBER 19, 2023

This article will discuss navigating the Comet LLMOps tool, the new LLM SDK, and much more. Comet LLMOps Comet’s LLMOps tools allow users to leverage the latest advancements in Prompt Management and query models in Comet to iterate quicker, identify performance bottlenecks, and visualize the internal state of the Prompt Chains.

Large Language Models 52

Large Language Models Metadata LLM Data Scientist 52

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.

Prompt Engineer 52

Prompt Engineer Prompt Engineering LLM Large Language Models 52

Heartbeat

FEBRUARY 8, 2024

The recent rise of Large Language Models (LLMs) has been a game changer for the ChatBot industry. These LLM-based bots have found various applications in various industries and have become the go-to information source for many people. The most basic chain is the LLMChain, which combines the LLM, prompt, and optionally an output parser.

Conversational AI 52

Conversational AI Chatbots LLM Prompt Engineer 52

Heartbeat

OCTOBER 19, 2023

Users can gain deep insights into the evolution of their prompts and responses. Prompt Playground Adventure: Within the LLMOps toolkit lies the innovative Prompt Playground, a dynamic space where Prompt Engineers can embark on expedited explorations. It can be installed using pip in a terminal or command line.

Large Language Models 52

Large Language Models LLM Metadata Deep Learning 52

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.

Prompt Engineer 52

Prompt Engineer Prompt Engineering Python Data Science 52

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. When thinking about a tool for metadata storage and management, you should consider: General business-related items : Pricing model, security, and support.

Machine Learning 134

Machine Learning Metadata Data Quality Data Scientist 134

Heartbeat

DECEMBER 15, 2023

This class, engineered for flexibility and efficiency, is a centralized conduit between the user's queries and the complex machinery of language model-based retrieval systems. Upon initialization, it accepts a pre-defined language model (LLM), setting the stage for sophisticated query-processing operations.

AI Chatbots 52

AI Chatbots LLM Chatbots NLP 52

Heartbeat

DECEMBER 13, 2023

You would change it to the following: tools = load_tools(tool_names, llm=llm) Defining Custom Tools Custom tools in LangChain are defined by the user to perform specific tasks or operations not provided by the native tools in the LangChain toolkit. You pass in a query (text string), and you get a result (text string).

LLM 52

LLM Metadata Deep Learning Automation 52

AWS Machine Learning Blog

NOVEMBER 29, 2023

However, harnessing this potential while ensuring the responsible and effective use of these models hinges on the critical process of LLM evaluation. An evaluation is a task used to measure the quality and responsibility of output of an LLM or generative AI service. Who needs to perform LLM evaluation?

LLM 82

LLM Data Scientist Algorithm Generative AI 82

Heartbeat

NOVEMBER 8, 2023

For example, you can create a chain that takes user input, formats it with a PromptTemplate, and then passes the formatted response to an LLM. The main differences between using an LLMChain versus directly passing a prompt to an LLM are: LLMChain allows chaining multiple prompts together, while directly passing a prompt only allows one.

LLM 52

LLM OpenAI Data Scientist Prompt Engineer 52

AWS Machine Learning Blog

APRIL 16, 2024

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. This adaptation is facilitated through the use of LLM prompts. The way you craft a prompt can profoundly influence the nature and usefulness of the AI’s response.

Data Scientist 89

Data Scientist Generative AI ML Machine Learning 89

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. Then comes prompt engineering. People just basically try different prompts.

Large Language Models 52

Large Language Models Prompt Engineer Prompt Engineering Neural Network 52

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. Then comes prompt engineering. People just basically try different prompts.

Large Language Models 52

Large Language Models Prompt Engineer Prompt Engineering Neural Network 52

Flipboard

NOVEMBER 24, 2023

Anthropic, an AI safety and research lab that builds reliable, interpretable, and steerable AI systems, is one of the leading AI companies that offers access to their state-of-the art LLM, Claude, on Amazon Bedrock. Anthropic offers both Claude and Claude Instant models, all of which are available through Amazon Bedrock.

ETL 112

ETL Prompt Engineer Prompt Engineering Generative AI 112