Categorization Metadata Prompt Engineering 
Unite.AI
NOVEMBER 20, 2024
The authors categorize traceable artifacts, propose key features for observability platforms, and address challenges like decision complexity and regulatory compliance. Artifacts: Track intermediate outputs, memory states, and prompt templates to aid debugging. Evaluation Artifacts: Benchmarks, feedback loops, and scoring metrics.
AWS Machine Learning Blog
MARCH 13, 2025
In this collaboration, the Generative AI Innovation Center team created an accurate and cost-efficient generative AIbased solution using batch inference in Amazon Bedrock , helping GoDaddy improve their existing product categorization system. Moreover, employing an LLM for individual product categorization proved to be a costly endeavor.
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AWS Machine Learning Blog
MARCH 20, 2025
Next, Amazon Comprehend or custom classifiers categorize them into types such as W2s, bank statements, and closing disclosures, while Amazon Textract extracts key details. With growing content libraries, media companies need efficient ways to categorize, search, and repurpose assets for production, distribution, and monetization.
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.
AWS Machine Learning Blog
JUNE 25, 2024
Manually analyzing and categorizing large volumes of unstructured data, such as reviews, comments, and emails, is a time-consuming process prone to inconsistencies and subjectivity. Operational efficiency Uses prompt engineering, reducing the need for extensive fine-tuning when new categories are introduced.
AWS Machine Learning Blog
NOVEMBER 7, 2024
Some components are categorized in groups based on the type of functionality they exhibit. Prompt catalog – Crafting effective prompts is important for guiding large language models (LLMs) to generate the desired outputs. Having a centralized prompt catalog is essential for storing, versioning, tracking, and sharing prompts.
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.
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