Remove Auto-complete Remove Metadata Remove Prompt Engineering
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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.

Metadata 131
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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. Various prompting techniques, such as Zero/Few Shot, Chain-of-Thought (CoT)/Self-Consistency, ReAct, etc.

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MLOps Landscape in 2023: Top Tools and Platforms

The MLOps Blog

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.

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Build a serverless meeting summarization backend with large language models on Amazon SageMaker JumpStart

AWS Machine Learning Blog

You can use large language models (LLMs), more specifically, for tasks including summarization, metadata extraction, and question answering. SageMaker endpoints are fully managed and support multiple hosting options and auto scaling. Complete the following steps: On the Amazon S3 console, choose Buckets in the navigation pane.

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Evaluate the reliability of Retrieval Augmented Generation applications using Amazon Bedrock

AWS Machine Learning Blog

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.

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Empowering Model Sharing, Enhanced Annotation, and Azure Blob Backups in NLP Lab

John Snow Labs

In this release, we’ve focused on simplifying model sharing, making advanced features more accessible with FREE access to Zero-shot NER prompting, streamlining the annotation process with completions and predictions merging, and introducing Azure Blob backup integration. Click “Submit” to finalize.

NLP 52
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LLMOps: What It Is, Why It Matters, and How to Implement It

The MLOps Blog

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).