Remove APIs Remove Benchmark Remove Chatbots
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Optimizing AI responsiveness: A practical guide to Amazon Bedrock latency-optimized inference

AWS Machine Learning

Consider benchmarking your user experience to find the best latency for your use case, considering that most humans cant read faster than 225 words per minute and therefore extremely fast response can hinder user experience. In such scenarios, you want to optimize for TTFT. Users prefer accurate responses over quick but less reliable ones.

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From RAG to fabric: Lessons learned from building real-world RAGs at GenAIIC – Part 2

AWS Machine Learning

A chatbot enables field engineers to quickly access relevant information, troubleshoot issues more effectively, and share knowledge across the organization. An alternative approach to routing is to use the native tool use capability (also known as function calling) available within the Bedrock Converse API.

APIs 120
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Evaluate RAG responses with Amazon Bedrock, LlamaIndex and RAGAS

AWS Machine Learning

Amazon Bedrock is a fully managed service that offers a choice of high-performing Foundation Models (FMs) from leading AI companies like AI21 Labs, Anthropic, Cohere, Meta, Stability AI, and Amazon via a single API, along with a broad set of capabilities you need to build generative AI applications with security, privacy, and responsible AI.

Metrics 78
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Build a contextual chatbot for financial services using Amazon SageMaker JumpStart, Llama 2 and Amazon OpenSearch Serverless with Vector Engine

AWS Machine Learning

Model choices – SageMaker JumpStart offers a selection of state-of-the-art ML models that consistently rank among the top in industry-recognized HELM benchmarks. Here’s how RAG operates: Data sources – RAG can draw from varied data sources, including document repositories, databases, or APIs. Lewis et al.

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Maximizing ROI with CPQ: 10 Best Practices for Sales Success

Cincom

Use APIs and middleware to bridge gaps between CPQ and existing enterprise systems, ensuring smooth data flow. Automate Price Calculations and Adjustments Utilize real-time pricing engines within CPQ to dynamically calculate prices based on market trends, cost fluctuations, and competitor benchmarks.

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Best practices to build generative AI applications on AWS

AWS Machine Learning

Whether creating a chatbot or summarization tool, you can shape powerful FMs to suit your needs. Amazon Bedrock is a fully managed service that offers a choice of high-performing foundation models (FMs) from leading AI companies like AI21 Labs, Anthropic, Cohere, Meta, Mistral AI, Stability AI, and Amazon via a single API.

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Build RAG applications using Jina Embeddings v2 on Amazon SageMaker JumpStart

AWS Machine Learning

You can use this tutorial as a starting point for a variety of chatbot-based solutions for customer service, internal support, and question answering systems based on internal and private documents. Long input-context length – Jina Embeddings v2 models support 8,192 input tokens.

Benchmark 121