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Let’s talk about Chat GPT in the Contact Center

CCNG

Continuous Improvement : Chat GPT can learn from interactions and customer feedback, enabling it to continuously improve its responses over time. In the end, writing scripts, using it for marketing or content and other simple tasks appear to be the main use cases right now.” says Fred.

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Use RAG for drug discovery with Knowledge Bases for Amazon Bedrock

AWS Machine Learning

The Retrieve and RetrieveAndGenerate APIs allow your applications to directly query the index using a unified and standard syntax without having to learn separate APIs for each different vector database, reducing the need to write custom index queries against your vector store.

APIs 112
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Testing times: testingRTC is the smart, synchronized, real-world scenario WebRTC testing solution for the times we live in.

Spearline

testingRTC creates faster feedback loops from development to testing. And testingRTC offers multiple ways to export these metrics, from direct collection from webhooks, to downloading results in CSV format using the REST API. Let’s take a look. testingRTC is created specifically for WebRTC. Happy days!

Scripts 98
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Generative AI, LLMs and AI Assistants: A Deep Dive into Customer Experience Technology

COPC

However, it’s important to note that LLMs lack true comprehension; their responses rely on their training and feedback. Experts interact with the AI, scoring its responses and providing corrective feedback. They respond based on their training and feedback loop, blurring the lines between knowledge and understanding.

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Streamline Your Business: Elevate Customer Service with Twilio Help Desk Contacts

TeleDirect

Their innovative APIs and cloud connection services are the perfect tools to improve our fantastic call centers. Plus, we develop unique scripts for our agents with your unique verbiage and branding, providing a more cohesive experience for your clients and leads. That’s why we became Twilio help desk partners.

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GPT-NeoXT-Chat-Base-20B foundation model for chatbot applications is now available on Amazon SageMaker

AWS Machine Learning

As a JumpStart model hub customer, you get improved performance without having to maintain the model script outside of the SageMaker SDK. has also undergone further fine-tuning via a small amount of feedback data. The inference script is prepacked with the model artifact. The deploy method may take a few minutes.

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Automatically generate impressions from findings in radiology reports using generative AI on AWS

AWS Machine Learning

In order to run inference through SageMaker API, make sure to pass the Predictor class. pre_trained_model = Model( image_uri=deploy_image_uri, model_data=pre_trained_model_uri, role=aws_role, predictor_cls=Predictor, name=pre_trained_name, env=large_model_env, ) # Deploy the pre-trained model.