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Evolving Trends in Prompt Engineering for Large Language Models (LLMs) with Built-in Responsible AI…

ODSC - Open Data Science

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. This trainable custom model can then be progressively improved through a feedback loop as shown above.

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A Guide to Mastering Large Language Models

Unite.AI

Large language models (LLMs) have exploded in popularity over the last few years, revolutionizing natural language processing and AI. From chatbots to search engines to creative writing aids, LLMs are powering cutting-edge applications across industries. Prompt engineering is crucial to steering LLMs effectively.

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Personalize your generative AI applications with Amazon SageMaker Feature Store

AWS Machine Learning Blog

Large language models (LLMs) are revolutionizing fields like search engines, natural language processing (NLP), healthcare, robotics, and code generation. Another essential component is an orchestration tool suitable for prompt engineering and managing different type of subtasks.

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Text-to-Music Generative AI : Stability Audio, Google’s MusicLM and More

Unite.AI

However, as technology advanced, so did the complexity and capabilities of AI music generators, paving the way for deep learning and Natural Language Processing (NLP) to play pivotal roles in this tech. Initially, the attempts were simple and intuitive, with basic algorithms creating monotonous tunes.

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Reinventing the data experience: Use generative AI and modern data architecture to unlock insights

AWS Machine Learning Blog

Solution overview A modern data architecture on AWS applies artificial intelligence and natural language processing to query multiple analytics databases. 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.

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Use custom metadata created by Amazon Comprehend to intelligently process insurance claims using Amazon Kendra

AWS Machine Learning Blog

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.

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How to Enhance Conversational Agents with Memory in Lang Chain

Heartbeat

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.