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Fine-Tuning vs Prompt Engineering on AWS: What’s the Right Approach?

Leave a Comment / Gen AI & AWS GenAI Stack /

Introduction So you’ve chosen your model, maybe Claude via Bedrock or Falcon on SageMaker.Now the next question hits:Should we fine-tune this model? Or can we just prompt it better?Choosing between fine-tuning and prompt engineering isn’t just technical, it’s strategic.Let’s explore when each approach makes sense in the AWS ecosystem, how they differ, and how to […]

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RAG on AWS: Retrieval-Augmented Generation Architecture & Best Practices

Leave a Comment / Gen AI & AWS GenAI Stack /

Introduction Large language models are brilliant, but they forget things.They can’t answer questions about your private docs or industry-specific data unless you fine-tune them or… use RAG.RAG (Retrieval-Augmented Generation) is the fastest, safest way to make GenAI models useful for your data, without retraining anything.In this post, we’ll explain how to build a RAG pipeline

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Getting Started with AWS GenAI Stack: What You Need to Know in 2025

Leave a Comment / Gen AI & AWS GenAI Stack /

Introduction Generative AI isn’t hype anymore; it’s a practical, transformative layer across industries. AWS has rapidly evolved to offer a full-stack suite for teams looking to build, deploy, and scale GenAI applications.If you’re exploring AWS for GenAI in 2025, this guide is your starting point. What is the AWS GenAI Stack? The AWS GenAI Stack

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How to Build Your First Custom LLM Application on AWS

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Introduction LLMs aren’t just for chatbots anymore.From intelligent agents to contract review to personalized summaries, custom LLM applications are reshaping workflows across every industry.But building one on AWS doesn’t have to be overwhelming. In this post, we’ll walk you through a step-by-step architecture to launch your first custom LLM app using AWS-native tools. Step 1:

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