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Benchmarking Amazon Nova and GPT-4o models with FloTorch

AWS Machine Learning

Using its enterprise software, FloTorch conducted an extensive comparison between Amazon Nova models and OpenAIs GPT-4o models with the Comprehensive Retrieval Augmented Generation (CRAG) benchmark dataset. FloTorch used these queries and their ground truth answers to create a subset benchmark dataset.

Benchmark 106
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Guest Blog: How to Benchmark Your Customer Support Metrics — A Quick Guide

ShepHyken

One of the best ways by which you can ensure your organization is consistently performing is by benchmarking customer support metrics. This involves the comparison of certain metrics to that the industry benchmarks and to your competitors. Pro tip 1: Get your team involved in a knowledge base project or set up micro-learning.

Benchmark 189
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How to Strategically Compete Against Your Peers… And Yourself

Beyond Philosophy

The methods used to understand competitors most often involve one or more approaches to benchmarking. Benchmarking goes beyond competitive analysis to interpret how peer organizations do what they do in terms of quality, time, cost and overall customer value dimensions. It is not copying the best.” It is not copying the best.”

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Evaluate RAG responses with Amazon Bedrock, LlamaIndex and RAGAS

AWS Machine Learning

Build sample RAG Documents are segmented into chunks and stored in an Amazon Bedrock Knowledge Bases (Steps 24). The solution consists of the following components: Evaluation dataset The source data for the RAG comes from the Amazon SageMaker FAQ , which represents 170 question-answer pairs.

Metrics 114
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Live Chat Benchmark Data & Best Practices for Financial Services & Banking

Comm100

To help you on this journey, this blog reveals the key financial services and banking metrics from our 2021 Live Chat Benchmark Report , alongside top live chat best practices that will help you to gain your clients’ trust and loyalty. 2021 Live Chat Benchmark Report – Download the report for exclusive industry and team size data.

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LLM-as-a-judge on Amazon Bedrock Model Evaluation

AWS Machine Learning

Amazon Bedrock , a fully managed service offering high-performing foundation models from leading AI companies through a single API, has recently introduced two significant evaluation capabilities: LLM-as-a-judge under Amazon Bedrock Model Evaluation and RAG evaluation for Amazon Bedrock Knowledge Bases. 0]}-{evaluator_model.split('.')[0]}-{datetime.now().strftime('%Y-%m-%d-%H-%M-%S')}"

Metrics 95
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LLM continuous self-instruct fine-tuning framework powered by a compound AI system on Amazon SageMaker

AWS Machine Learning

Besides the efficiency in system design, the compound AI system also enables you to optimize complex generative AI systems, using a comprehensive evaluation module based on multiple metrics, benchmarking data, and even judgements from other LLMs. Complete the following steps: Load the dataset for evaluation in the Example data type.