Unite.AI

JANUARY 23, 2024

Prompting Rather than inputs and outputs, LLMs are controlled via prompts – contextual instructions that frame a task. Prompt engineering is crucial to steering LLMs effectively. Hybrid retrieval combines dense embeddings and sparse keyword metadata for improved recall.

Large Language Models 130

Large Language Models Prompt Engineer Prompt Engineering LLM 130

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. Prompt engineering Prompt engineering enables you to instruct LLMs to generate suggestions, explanations, or completions of text in an interactive way.

Prompt Engineer 89

Prompt Engineer Prompt Engineering Large Language Models LLM 89

AWS Machine Learning Blog

MAY 30, 2024

The workflow for NLQ consists of the following steps: A Lambda function writes schema JSON and table metadata CSV to an S3 bucket. The wrapper function reads the table metadata from the S3 bucket. The wrapper function creates a dynamic prompt template and gets relevant tables using Amazon Bedrock and LangChain.

Metadata 93

Metadata Generative AI LLM Prompt Engineer 93

Mlearning.ai

JULY 8, 2023

Prompt Engineering — this is where figuring out what is the right prompt to use for the problem. Model selection can be based on use case, performance, cost, latency, etc Test and validate the prompt engineering and see the output with application is as expected. original article —  Samples2023/LLM/llmops.md

Large Language Models 52

Large Language Models LLM Prompt Engineer Prompt Engineering 52

Bugra Akyildiz

MARCH 16, 2024

Of course, all of the things that control vector does can be done through prompt engineering as well as one can consider control vector to be an “addition” to the prompt that is provided by the user. This is where metadata comes in. Metadata is essentially data about data.

Metadata 59

Metadata LLM Machine Learning Python 59

AWS Machine Learning Blog

MAY 21, 2024

However, when the article is complete, supporting information and metadata must be defined, such as an article summary, categories, tags, and related articles. While these tasks can feel like a chore, they are critical to search engine optimization (SEO) and therefore the audience reach of the article.

Generative AI 102

Generative AI Automation Prompt Engineer Prompt Engineering 102

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.

Generative AI 105

Generative AI LLM Natural Language Processing Metadata 105

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

John Snow Labs

OCTOBER 16, 2023

solves this problem by extracting metadata during the data preparation process. File, page, and section metadata enable users to determine the source of a response, while document publication and modification dates allow retrieval systems to bias toward more recent data. Unstructured.IO Unstructured.IO

Large Language Models 52

Large Language Models NLP Metadata LLM 52

Mlearning.ai

FEBRUARY 26, 2024

Post-Retrieval Next, the RAG model augments the user input (or prompts) by adding the relevant retrieved data in context (query + context). This step uses prompt engineering techniques to communicate effectively with the LLM. Remove unnecessary information such as special characters, unwanted metadata, or text.

Metadata 52

Metadata Large Language Models LLM Neural Network 52

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.

Chatbots 91

Chatbots Prompt Engineer Prompt Engineering Large Language Models 91

Heartbeat

SEPTEMBER 19, 2023

Comet’s LLMOps tool provides an intuitive and responsive view of our prompt history. Prompt Playground: With the LLMOps tool comes the new Prompt Playground, which allows Prompt Engineers to iterate quickly with different Prompt Templates and understand the impact on different contexts.

Large Language Models 52

Large Language Models Metadata LLM Data Scientist 52

John Snow Labs

MAY 21, 2024

Private, on-premise, high-compliance prompt engineering Protecting Personal Health Information (PHI) is paramount in the healthcare sector. Bug Fixes and Improvements Support for multiple spaces and tabs in Label Metadata Previously, within the label metadata, multiple spaces were considered a single space, and tabs were not supported.

Generative AI 52

Generative AI AI Modeling Metadata NLP 52

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. However, these databases must have their metadata registered with the AWS Glue Data Catalog.

Generative AI 69

Generative AI Metadata LLM Large Language Models 69

Heartbeat

FEBRUARY 8, 2024

LangChain loads training data in the form of ‘Documents’, and these contain text from various sources and their respective metadata. Tracking and Monitoring with Comet When creating applications with LLMs, most of the time is spent on prompt engineering rather than training the models.

Conversational AI 52

Conversational AI Chatbots LLM Prompt Engineer 52

The MLOps Blog

MARCH 12, 2024

Tools range from data platforms to vector databases, embedding providers, fine-tuning platforms, prompt engineering, evaluation tools, orchestration frameworks, observability platforms, and LLM API gateways. with efficient methods and enhancing model performance through prompt engineering and retrieval augmented generation (RAG).

Prompt Engineer 52

Prompt Engineer Prompt Engineering LLM Large Language Models 52

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. ") How the data is logged in Comet LLM: Input/ Output are log in Comet LLM (Image by the Author) Also the metadata in LLM 2. It seems to be a problem with the zipper.

Metadata 52

Metadata LLM OpenAI Chatbots 52

IBM Journey to AI blog

JULY 17, 2023

offers a Prompt Lab, where users can interact with different prompts using prompt engineering on generative AI models for both zero-shot prompting and few-shot prompting. These Slate models are fine-tuned via Jupyter notebooks and APIs. To bridge the tuning gap, watsonx.ai

Machine Learning 157

Machine Learning AI AI Metadata 157

Unite.AI

SEPTEMBER 25, 2023

MusicLM Performance, Image source: here Stability Audio Stability AI last week introduced “ Stable Audio ” a latent diffusion model architecture conditioned on text metadata alongside audio file duration and start time.

Generative AI 296

Generative AI Deep Learning AI AI 296

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 Scientist Data Quality 134

Mlearning.ai

JUNE 17, 2023

outputsearch)), Char(10),""), """","") Goal is to use semantic search get the top 3 docs and then pass that with prompt engineering to chatgpt and get the response back and display it in power apps original article —  Samples2023/pinesearchgpt.md

Metadata 52

Metadata Prompt Engineer Prompt Engineering AI 52

Flipboard

NOVEMBER 24, 2023

By analyzing millions of metadata elements and data flows, Iris could make intelligent suggestions to users, democratizing data integration and allowing even those without a deep technical background to create complex workflows. Iris was designed to use machine learning (ML) algorithms to predict the next steps in building a data pipeline.

ETL 112

ETL Prompt Engineer Prompt Engineering Generative AI 112

Flipboard

DECEMBER 6, 2023

Given the right context, metadata, and instructions, a well-selected general purpose LLM can produce good-quality SQL as long as it has access to the right domain-specific context. Further performance optimization involved fine-tuning the query generation process using efficient prompt engineering techniques.

Chatbots 130

Chatbots LLM Prompt Engineer Prompt Engineering 130

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. comet_llm.log_prompt( prompt="Hey,What's your name?

Large Language Models 52

Large Language Models LLM Metadata Deep Learning 52

AWS Machine Learning Blog

SEPTEMBER 1, 2023

Strong domain knowledge for tuning, including prompt engineering, is required as well. Consumers – Users who interact with generative AI services from providers or fine-tuners by text prompting or a visual interface to complete desired actions. Only prompt engineering is necessary for better results.

Generative AI 97

Generative AI Prompt Engineer Prompt Engineering AI 97

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.

LLM 95

LLM Prompt Engineer Prompt Engineering Large Language Models 95

Viso.ai

MARCH 12, 2024

Metadata for Transparency: C2PA metadata (identifying AI-generated content) will be included in future deployments. Leveraging Existing Safety Methods: Existing safety measures from DALL-E 3 will be applied to Sora, including: Text prompt filtering to prevent harmful content generation.

OpenAI 52

OpenAI Metadata Deep Learning Prompt Engineer 52

AWS Machine Learning Blog

OCTOBER 24, 2023

For example, we can follow prompt engineering best practices to fine-tune an LLM to format dates into MM/DD/YYYY format, which may be compatible with a database DATE column. The following code block shows an example of how this is done using an LLM and prompt engineering.

IDP 107

IDP LLM Prompt Engineer Prompt Engineering 107

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.

AI 98

AI AI AI Tools Prompt Engineer 98

AWS Machine Learning Blog

MAY 17, 2023

You can use large language models (LLMs), more specifically, for tasks including summarization, metadata extraction, and question answering. These models span tasks like text-to-text, text-to-image, text-to-embedding, and more. Amazon SageMaker JumpStart is an ML hub that can helps you accelerate your ML journey.

Large Language Models 68

Large Language Models Auto-complete Generative AI ML 68

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

John Snow Labs

OCTOBER 12, 2023

It also pre-fills the model form with model metadata, usage code, and example results as much as possible. While NLP Lab has previously provided support for prompt engineering, the feature was restricted to users with license keys.

NLP 52

NLP Auto-complete Auto-classification Prompt Engineer 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.

Large Language Models 92

Large Language Models LLM Machine Learning Automation 92

Heartbeat

NOVEMBER 22, 2023

metadata : (Optional) Additional metadata to associate with the evaluation. Parameters: prediction : The output string from the first model. prediction_b : The output string from the second model. input : (Optional) The input or task string that led to the predictions. reference : (Optional) A reference string for comparison.

LLM 52

LLM Explainability Metadata Deep Learning 52

Snorkel AI

MARCH 2, 2023

This goes into the model risk management in analyzing the metadata around the model, whether it’s fit for purpose with automated or human-in-the-loop capabilities. We have adaptation, we have experimentation, and in the training and hyperparameter optimization phases, deploying, monitoring and managing, and prompt engineering.

Large Language Models 52

Large Language Models Prompt Engineer Prompt Engineering Neural Network 52

Snorkel AI

MARCH 2, 2023

This goes into the model risk management in analyzing the metadata around the model, whether it’s fit for purpose with automated or human-in-the-loop capabilities. We have adaptation, we have experimentation, and in the training and hyperparameter optimization phases, deploying, monitoring and managing, and prompt engineering.

Large Language Models 52

Large Language Models Prompt Engineer Prompt Engineering Neural Network 52

Heartbeat

DECEMBER 13, 2023

', args_schema=None, return_direct=False, verbose=False, callbacks=None, callback_manager=None, tags=None, metadata=None, handle_tool_error=False, api_wrapper=WikipediaAPIWrapper(wiki_client=<module 'wikipedia' from '/usr/local/lib/python3.10/dist-packages/wikipedia/__init__.py'>, Input should be a search query.',

LLM 52

LLM Metadata Deep Learning Automation 52