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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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GenAI on a Budget: Cost-Optimization Strategies Using AWS Tools

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Introduction Building GenAI apps sounds expensive.And sometimes, it is, especially if you jump straight into fine-tuning LLMs or spinning up GPU clusters without a plan.But here’s the good news: AWS offers several ways to run GenAI workloads cost-effectively if you know where to look.In this post, we’ll share practical strategies to build, test, and deploy

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Multi-Modal Models on AWS: What’s Possible Today?

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Introduction In 2025, GenAI is no longer limited to just words on a screen.From images to text, audio to documents, multi-modal models are now shaping how we interact with AI-powered applications.So, where does AWS stand in this multi-modal future?Let’s explore what’s possible right now on AWS when it comes to multi-modal GenAI and how to

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

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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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Why Vector Databases Matter for GenAI (and Where AWS Fits)

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Introduction LLMs are powerful, but they’re also forgetful.Out of the box, they have no knowledge of your PDFs, chat logs, or product catalog. That’s where vector databases come in.Vector databases are the memory layer of GenAI, especially for Retrieval-Augmented Generation (RAG) applications.This post explains what vector DBs are, why they matter, and which AWS-native (or

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