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The True Cost of Neglecting Quality Assurance: Lessons from Recent Tech Failures

Perficient

The Importance of Quality Assurance in Software Development The pressure to deliver software quickly and cost-effectively is among the highest priorities of business leaders. However, recent high-profile tech failures serve as stark reminders of the critical importance of robust Quality Assurance (QA) practices.

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Generative AI in Data and Quality Assurance (QA): Transforming Processes

Perficient

Automating tasks to improve performance and accuracy forreducing errors, and predictiveanalytics viasynthetic datacreationisa waytodistinguish oneselfas thefoundationofcertain emergingdigital transformation strategies today.

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Emerging Software Testing and Quality Assurance Trends to Consider in 2021

Radixweb

For most businesses, the pandemic has brought in a moment to rethink the existing strategies for quality assurance and software testing for the year 2021.

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CIOs are bullish on AI agents. IT employees? Not so much.

CIO

Frontline AI practitioners have likely seen the amount of customization, quality assurance, and maintenance required to make a somewhat functional agentic workflow, Mikhailov says. Deployment takes a lot of work Some of the resistance is likely coming from IT professionals who comprehend the work needed to deploy them, Mikhailov says.

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Struggling to Scale Test Coverage Under Pressure?

When test coverage falls behind release velocity, quality suffers, and your team feels the consequences. This guide outlines when it makes sense to outsource quality assurance (QA), the risks to watch for, and how to scale testing without increasing headcount or slowing down engineering.

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Beyond automation: Realizing the full potential of agentic AI in the enterprise

CIO

We then explore design and orchestration strategies, discuss human oversight and governance, and outline practical examples to illustrate deployment and scaling. Agents can refine their strategies or models, using new data or sources to improve accuracy, efficiency or other user goals, including ones that may not have been originally stated.

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Evaluating RAG applications with Amazon Bedrock knowledge base evaluation

AWS Machine Learning - AI

By the end of this post, you will understand how the latest Amazon Bedrock evaluation features can streamline your approach to AI quality assurance, enabling more efficient and confident development of RAG applications. Cost and speed considerations The efficiency of RAG systems depends on model selection and usage patterns.