Categorization Data Extraction Prompt Engineering 
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. Additional processing is needed to standardize formats, manage JSON outputs, and align data fields, often requiring manual integration and multiple API calls.
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. We also examine the uplift from fine-tuning an LLM for a specific extractive task. In this example, you explicitly set the instance type to ml.g5.48xlarge.
This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.
ODSC - Open Data Science
OCTOBER 27, 2023
The core idea behind this phase is automating the categorization or classification using AI. We use Amazon Textract’s document extraction abilities with LangChain to get the text from the document and then use prompt engineering to identify the possible document category.
AWS Machine Learning Blog
NOVEMBER 26, 2024
Task 1: Query generation from natural language This task’s objective is to assess a model’s capacity to translate natural language questions into SQL queries, using contextual knowledge of the underlying data schema. We used prompt engineering guidelines to tailor our prompts to generate better responses from the LLM.
AWS Machine Learning Blog
JANUARY 7, 2025
Machine learning (ML) classification models offer improved categorization, but introduce complexity by requiring separate, specialized models for classification, entity extraction, and response generation, each with its own training data and contextual limitations. Built-in conditional logic handles different processing paths.
Expert insights. Personalized for you.
106 
Let's personalize your content