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ROUGE: Decoding the Quality of Machine-Generated Text

Analytics Vidhya

Imagine an AI that can write poetry, draft legal documents, or summarize complex research papersbut how do we truly measure its effectiveness? As Large Language Models (LLMs) blur the lines between human and machine-generated content, the quest for reliable evaluation metrics has become more critical than ever.

Metrics 199
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Unbundling the Graph in GraphRAG

O'Reilly on Data

Here’s a simple rough sketch of RAG: Start with a collection of documents about a domain. Split each document into chunks. While RAG leverages nearest neighbor metrics based on the relative similarity of texts, graphs allow for better recall of less intuitive connections. at Facebook—both from 2020.

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The Race For Data Quality in a Medallion Architecture

DataKitchen

Finally, the challenge we are addressing in this document – is how to prove the data is correct at each layer.? Similarly, downstream business metrics in the Gold layer may appear skewed due to missing segments, which can impact high-stakes decisions. How do you ensure data quality in every layer?

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The Ultimate Guide to Modern Data Quality Management (DQM) For An Effective Data Quality Control Driven by The Right Metrics

datapine

6) Data Quality Metrics Examples. Reporting being part of an effective DQM, we will also go through some data quality metrics examples you can use to assess your efforts in the matter. The data quality analysis metrics of complete and accurate data are imperative to this step. Table of Contents. 2) Why Do You Need DQM?

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Use Amazon Kinesis Data Streams to deliver real-time data to Amazon OpenSearch Service domains with Amazon OpenSearch Ingestion

AWS Big Data

For agent-based solutions, see the agent-specific documentation for integration with OpenSearch Ingestion, such as Using an OpenSearch Ingestion pipeline with Fluent Bit. This includes adding common fields to associate metadata with the indexed documents, as well as parsing the log data to make data more searchable.

Metadata 121
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Migrate from Amazon Kinesis Data Analytics for SQL to Amazon Managed Service for Apache Flink and Amazon Managed Service for Apache Flink Studio

AWS Big Data

Amazon Kinesis Data Analytics for SQL is a data stream processing engine that helps you run your own SQL code against streaming sources to perform time series analytics, feed real-time dashboards, and create real-time metrics. AWS has made the decision to discontinue Kinesis Data Analytics for SQL, effective January 27, 2026.

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Escaping POC Purgatory: Evaluation-Driven Development for AI Systems

O'Reilly on Data

Business value : Once we have a rubric for evaluating our systems, how do we tie our macro-level business value metrics to our micro-level LLM evaluations? Any scenario in which a student is looking for information that the corpus of documents can answer. Wrong document retrieval : Debug chunking strategy, retrieval method.

Testing 174