The Evolution of Generative AI: A New Era of Reasoning and Scalability
The next wave of generative AI (GenAI) is being driven by a focus on reasoning. This shift is underpinned by advancements in training and inference, which provide the infrastructure necessary for scalability, reliability, and the development of new application classes. At the AWS Summit in Johannesburg, Willem Visser, Amazon Web Services (AWS) VP for the EC2 cloud platform, emphasized how this transformation has led to the creation of Amazon Bedrock—a service designed to make it easier for developers to build and scale GenAI applications on AWS.
What Is Amazon Bedrock?
Amazon Bedrock is a managed service that offers access to a wide range of high-performing foundation models (FMs) from leading providers such as AI21 Labs, Anthropic, Cohere, Stability AI, and Amazon’s own models. It simplifies the process of developing GenAI applications by allowing developers to use these models through a single API. This central hub enables businesses to create GenAI applications without the burden of managing their own infrastructure, making it an attractive option for organizations looking to leverage AI without significant upfront investment.
Choosing the Right Model
Visser highlighted that the first step in building a successful GenAI application is selecting the right model. The field of FMs is advancing rapidly, with more capable, cost-effective, and faster models being released frequently. He noted that there is no one-size-fits-all solution, and AWS offers a broad selection of fully managed FMs across various providers to help users find the best fit for their needs.
“Without the need to manage and scale your infrastructure, you’re free to build and experiment with the latest models without compromising on security or performance,” said Visser. He also mentioned Amazon Nova, a family of FMs designed to offer new options across quality, speed, and cost.
Customization and Integration
AWS recently introduced the ability to customize Nova models using SageMaker AI, a service that helps data scientists and developers build, train, and deploy machine learning models. This feature allows businesses to choose a model that aligns with their specific use cases. Incorporating custom data is a critical step in extracting value from AI, and one common approach is retrieval-augmented generation (RAG).
However, RAG was originally optimized for unstructured data. To address this, Bedrock now supports fully managed end-to-end RAG across different data types, resulting in more relevant, accurate, and explainable responses for users.
Trust, Security, and Affordability
Trust remains a key factor in enterprise adoption of GenAI. Bedrock includes guardrails that allow organizations to block harmful inputs and outputs, ensuring that applications align with company policies and brand values. Additionally, AWS has introduced automated reasoning checks within these guardrails to reduce factual errors and minimize the risk of hallucinations in AI-generated responses.
Cost is another major challenge in GenAI adoption. Techniques like model distillation—where smaller models learn from larger ones—improve performance while lowering costs. According to AWS, distilled models in Bedrock can be up to five times faster and 75% cheaper than their larger counterparts.
Bedrock also features intelligent prompt grounding, which allows multiple models to be designated for an application. The system then routes each request to the most appropriate model, reducing costs by up to 50% without sacrificing accuracy.
Real-World Applications of Bedrock
At the AWS Summit’s GenAI Zone, demonstrations showcased how Bedrock goes beyond basic image generation or text transcription. Daniel Schormann, an Amazon Connect specialist solutions architect, presented the GenAI Augmented Contact Centre exhibit, highlighting how Bedrock can extract insights, automate processes, and integrate seamlessly with business systems.
“What we want to do is take that call recording, get a transcript, and then extract insights from it,” Schormann explained. Once the raw text is processed through Bedrock with engineered prompts, the system can deliver more than just a summary. It can generate structured JSON objects, which are easy to store, query, and link with other applications.
“This is where Bedrock shows its strength: turning manual, resource-heavy tasks into automated ones,” Schormann said. For example, a quality assurance check that once required staff to listen to a 20-minute call can now be completed automatically, with results routed directly to training workflows for agents.
The Business Value of GenAI
The efficiency and scalability offered by GenAI make it a compelling commercial tool. Schormann pointed out that automating processes can significantly reduce costs. “If you can make your agents more efficient, you can make your processes more efficient, and you can automate. You’re suddenly taking a $500 call and making it $200, saving three-fifths of the entire cost.”
This level of efficiency not only improves operational performance but also allows teams to focus on more valuable tasks. As GenAI continues to evolve, services like Amazon Bedrock are playing a crucial role in making these technologies accessible and effective for enterprises worldwide.
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