Tag: Amazon Bedrock
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AWS Generative AI vs the Field, the Verdict (AWS Gen AI Series, Part 30)
After thirty parts on the AWS generative AI stack, the verdict: where Bedrock, Amazon Nova, and AWS silicon earn the default, and the exact cases where Azure, Google Cloud, or IBM watsonx fit better.
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Bedrock Reference Architectures for Chatbot, RAG, Agentic, and Batch (AWS Gen AI Series, Part 29)
Most AWS generative AI features are one of four shapes: chatbot, RAG, agentic, or batch. Here is how each maps to Amazon Bedrock services, what it costs, and which one to reach for first.
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Amazon Bedrock Observability with CloudWatch and Invocation Logging (AWS Gen AI Series, Part 25)
Bedrock ships almost no history by default. Here is how I turn on model invocation logging, pick the CloudWatch metrics worth an alarm, and pull token cost per model straight from the logs.
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Amazon Bedrock Model Evaluation, Automatic to LLM-as-a-Judge (AWS Gen AI Series, Part 23)
Amazon Bedrock has three ways to score a model: programmatic metrics, an LLM judge, and human review. Here is what each measures, what it costs, and how to run a judge job without fooling yourself.
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Amazon Bedrock Multi-Agent Collaboration, Supervisor and Collaborator Agents (AWS Gen AI Series, Part 21)
One supervisor agent, a few specialist collaborators, and a hard step budget. How multi-agent collaboration works on Amazon Bedrock in 2026, what it costs in latency and tokens, and why the Agents Classic cutoff changes where you should build.
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Amazon Bedrock Model Distillation, End to End (AWS Gen AI Series, Part 17)
Amazon Bedrock Model Distillation trains a small student model to answer like a big teacher for a narrow task. Here is how the job runs, which model pairs are allowed, and why Provisioned Throughput, not the training, decides the cost.
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Amazon Bedrock Fine-Tuning and Continued Pre-Training (AWS Gen AI Series, Part 16)
When a bigger prompt stops paying off, you change the model itself. A practical walk through fine-tuning and continued pre-training on Amazon Bedrock: which models qualify, what a job costs, and how Nova on-demand hosting changed the math.
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Amazon Bedrock Prompt Management, Flows, and Prompt Caching (AWS Gen AI Series, Part 15)
Prompt caching, Prompt Management, and Bedrock Flows get grouped together and confused constantly. What each one does, what caching actually saves, and which to reach for.
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Amazon Bedrock Guardrails, Content Filters, and Grounding Checks (AWS Gen AI Series, Part 14)
Amazon Bedrock Guardrails inspects text into and out of a model across six policies. Where each fits, how to call it inline and standalone, what it costs, and where it trips you in production.
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Amazon Bedrock Agents, Action Groups, and the AgentCore Shift (AWS Gen AI Series, Part 13)
How Bedrock agents turn one question into several model calls, how action groups and return of control work, what a request really costs, and why new builds now start on AgentCore.
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Amazon Bedrock Knowledge Bases and Managed RAG, End to End (AWS Gen AI Series, Part 12)
How Amazon Bedrock Knowledge Bases turn your documents into retrieval augmented generation, which vector store and chunking to pick, and why the bill is dominated by a vector-store floor, not tokens.
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About the Author
Dr Pranay Jha
Dr. Pranay Jha is a Cloud and AI Consultant with 18+ years of experience in hybrid cloud, virtualization, and enterprise infrastructure transformation. He specializes in VMware technologies, multi-cloud strategy, and Generative AI solutions. He holds a PhD in Computer Applications with research focused on Cloud and AI, has published multiple research papers, and has been a VMware vExpert since 2016 and a VMUG Community Leader.
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