AWS Machine Learning Blog

JUNE 25, 2024

Operational efficiency Uses prompt engineering, reducing the need for extensive fine-tuning when new categories are introduced. Explainability Provides explanations for its predictions through generated text, offering insights into its decision-making process.

Automation 126

Automation Prompt Engineer Prompt Engineering Categorization 126

John Snow Labs

OCTOBER 16, 2023

.” – Carlos Rodriguez Abellan, Lead NLP Engineer at Fujitsu “The main obstacles to applying LLMs in my current projects include the cost of training and deploying LLM models, lack of data for some tasks, and the difficulty of interpreting and explaining the results of LLM models.” Unstructured.IO Unstructured.IO

Large Language Models 52

Large Language Models NLP Metadata Data Scarcity 52

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. Create a new project.

Large Language Models 52

Large Language Models Metadata LLM Data Scientist 52

AWS Machine Learning Blog

MAY 21, 2024

We dive into the technical aspects of our implementation and explain our decision to choose Amazon Bedrock as our foundation model provider. However, when the article is complete, supporting information and metadata must be defined, such as an article summary, categories, tags, and related articles.

Generative AI 129

Generative AI Prompt Engineer Prompt Engineering Metadata 129

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. The user receives an English answer to their prompt, querying data from different databases.

Generative AI 88

Generative AI Metadata LLM Large Language Models 88

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

The MLOps Blog

JUNE 27, 2023

This includes features for model explainability, fairness assessment, privacy preservation, and compliance tracking. The platform also offers features for hyperparameter optimization, automating model training workflows, model management, prompt engineering, and no-code ML app development. Can you render audio/video?

Metadata 134

Metadata Machine Learning Data Scientist Data Quality 134

AWS Machine Learning Blog

OCTOBER 24, 2023

Solution overview The following diagram is a high-level reference architecture that explains how you can further enhance an IDP workflow with foundation models. 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.

IDP 135

IDP LLM Prompt Engineer Prompt Engineering 135

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

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

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 Large Language Models LLM 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. comet_llm.log_prompt( prompt="Hey,What's your name?

Large Language Models 52

Large Language Models Metadata LLM Chatbots 52

AWS Machine Learning Blog

JULY 24, 2024

You can customize the model using prompt engineering, Retrieval Augmented Generation (RAG), or fine-tuning. It can also be done at scale, as explained in Operationalize LLM Evaluation at Scale using Amazon SageMaker Clarify and MLOps services. You can then select the best model based on the evaluation results.

LLM 130

LLM Generative AI ML Natural Language Processing 130

Heartbeat

NOVEMBER 22, 2023

metadata : (Optional) Additional metadata to associate with the evaluation. Returns: The method returns a dictionary with the following keys: reasoning : Explains why one prediction is preferred over the other. ", input="Explain the theory of relativity in a sentence.", kwargs : Additional keyword arguments.

LLM 52

LLM Explainability Metadata Deep Learning 52

AWS Machine Learning Blog

JUNE 20, 2024

Additionally, evaluation can identify potential biases, hallucinations, inconsistencies, or factual errors that may arise from the integration of external sources or from sub-optimal prompt engineering. In this case, the model choice needs to be revisited or further prompt engineering needs to be done.

Auto-classification 132

Auto-classification LLM Prompt Engineer Prompt Engineering 132

Heartbeat

NOVEMBER 24, 2023

A Document is a piece of text with associated metadata. We’re committed to supporting and inspiring developers and engineers from all walks of life. 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

Heartbeat

DECEMBER 15, 2023

Let’s explain exactly how it works and what this means for embedding documents into a vector database. ", "What alternative reasons might explain why students cheat, according to the article?", We’re committed to supporting and inspiring developers and engineers from all walks of life.

AI Chatbots 52

AI Chatbots LLM Chatbots NLP 52

Heartbeat

NOVEMBER 8, 2023

This gives you access to metadata like the number of tokens used. We’re committed to supporting and inspiring developers and engineers from all walks of life. '}] generate is similar to apply, except it returns an LLMResult instead of a string. We pay our contributors, and we don’t sell ads.

LLM 52

LLM OpenAI Data Scientist Prompt Engineer 52

Viso.ai

MARCH 12, 2024

Education and Training: Explainer videos and simulations that can be generated for children, students, and employees for interactive learning. Metadata for Transparency: C2PA metadata (identifying AI-generated content) will be included in future deployments. will test Sora to identify potential risks.

OpenAI 52

OpenAI Metadata Deep Learning ChatGPT 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

Bugra Akyildiz

JULY 4, 2024

The article concludes by explaining that deeper language programs in DSPy, like deeper neural networks, can be more effective. Metadata, callbacks, and data format conversions. They consider this result exciting given the minimal effort required. Store different models and configs as simple connection strings just like databases.

LLM 52

LLM Python Automation Data Analysis 52