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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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Beyond “Prompt and Pray”

O'Reilly on Data

Your companys AI assistant confidently tells a customer its processed their urgent withdrawal requestexcept it hasnt, because it misinterpreted the API documentation. These are systems that engage in conversations and integrate with APIs but dont create stand-alone content like emails, presentations, or documents.

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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. One more embellishment is to use a graph neural network (GNN) trained on the documents. Chunk your documents from unstructured data sources, as usual in GraphRAG. at Facebook—both from 2020.

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What is Levenshtein Distance?

Analytics Vidhya

Introduction As you work on a significant document, let’s say you see you’ve spelled a word incorrectly. Now for the intriguing Levenshtein Distance: it measures the amount of work needed to change one sequence into another, providing an effective tool for […] The post What is Levenshtein Distance?

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How sklearn’s Tfidfvectorizer Calculates tf-idf Values

Analytics Vidhya

Overview In NLP, tf-idf is an important measure and is used by algorithms like cosine similarity to find documents that are similar to a given search query. This article was published as a part of the Data Science Blogathon. Here in this blog, we will try to break tf-idf and see how sklearn’s TfidfVectorizer calculates […].

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Escaping POC Purgatory: Evaluation-Driven Development for AI Systems

O'Reilly on Data

How will you measure success? Any scenario in which a student is looking for information that the corpus of documents can answer. So now we have a user persona, several scenarios, and a way to measure success. Wrong document retrieval : Debug chunking strategy, retrieval method. We asked them: Who are you building it for?

Testing 168
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Where CIOs should place their 2025 AI bets

CIO Business Intelligence

Deloittes State of Generative AI in the Enterprise reports nearly 70% have moved 30% or fewer of their gen AI experiments into production, and 41% of organizations have struggled to define and measure the impacts of their gen AI efforts.