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Call Center Agent Feedback: Tips & Best Practices for Providing Effective Agent Feedback

Callminer

Providing feedback to agents in your call center is entirely needed to maintain and improve a quality facility. However, knowing how to deliver feedback can be tricky. Unfortunately, there are a number of pitfalls that can derail the process of delivering effective feedback.

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The Art of Delivering Feedback

Stafford Communications

The Art Of Delivering Feedback By: CJ Stafford , President One of the simple truths of people management – in any industry – is that your employees require feedback. The ability to effectively deliver (and humbly receive) constructive feedback is a must-have for anyone with supervisory responsibility. Make it private.

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

AWS Machine Learning

Construct the inference request as a JSON payload and use it to query the endpoints for the pre-trained and fine-tuned models. Furthermore, setting up a feedback loop for this application would enable radiologists to improve the performance of the model over time.

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The Art of Delivering Feedback

Stafford Communications

The Art Of Delivering Feedback. One of the simple truths of people management – in any industry – is that your employees require feedback. The ability to effectively deliver (and humbly receive) constructive feedback is a must-have for anyone with supervisory responsibility. By: CJ Stafford , President. Make it private.

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Is the Contact Center a Good Career? Tenured Agents Say Yes

Balto

“When I worked in service roles, I had a script, and I knew what I had to do to have a successful social interaction with a customer. This helped me build confidence through a body of evidence — you use your script correctly as a waitress and you get a dopamine hit in the form of a tip. Retrieved February 7, 2023, from [link] Balto.

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FMOps/LLMOps: Operationalize generative AI and differences with MLOps

AWS Machine Learning

Some models may be trained on diverse text datasets like internet data, coding scripts, instructions, or human feedback. We use the question “What if the Suez Canal had never been constructed?” The response is as follows: Question: What if the Suez Canal had never been constructed?

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Reduce inference time for BERT models using neural architecture search and SageMaker Automated Model Tuning

AWS Machine Learning

One example is an online retailer who deploys a large number of inference endpoints for text summarization, product catalog classification, and product feedback sentiment classification. Then we construct a request metadata and record the start time to be used for load testing. training.py ).

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