Robert Johns | 04 Dec, 2023

What Is Amazon Q? Is It Just Another ChatGPT Clone?

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What is Amazon Q? Is this just another ChatGPT clone or something more?

Fresh off the 2024 AWS re:Invent conference, Amazon Q was unveiled as the latest generative AI product vying for the crown of best LLM.

But what is Amazon Q? And why should developers like you and me care about Amazon Q?

These are great questions!

If you want the TL-DR, Amazon Q is a lot like a ChatGPT product for AWS. This means it’s perfect if you’re a cloud developer who’s heavily invested in the Amazon Web Services platform.

But there’s much more to Amazon Q than meets the eye, which I‘ll cover in this article.

One thing’s for sure: Amazon is investing heavily into generative AI, with Amazon Q, Amazon Bedrock, and Amazon Titan for image generation.

Let’s dive in to see why this matters and how you can use these products to level up your development with AWS.

What Is Amazon Q?

Amazon QAWS re:Invent is always one of the best places to keep abreast of the latest developments in the AWS cloud ecosystem, and this year, the theme in Las Vegas was AI.

At the center of this was the announcement of a new generative AI tool, Amazon Q.

This is unsurprising, as 2024 has been a banner year in tech for AI tools like ChatGPT for OpenAI and Microsoft and Bard for Google.

But what is Amazon Q?

For right now, the best way to describe Amazon Q is a ChatGPT-style product for AWS.

Now, Amazon may not appreciate me making that comparison, but I think the majority of us find it easier to relate LLMs and AI tools to ChatGPT, as it’s kind of like the iPhone of AI tools.

But let’s dig a little deeper, shall we?

To start with, Amazon Q is touted as a new type of generative AI-powered assistant designed for business use and enterprise systems.

But I think it helps to approach this from two angles: general business use cases and development.

General Business Use Cases For Amazon Q

The official definition of Amazon Q is a generative AI-powered assistant that’s been tailored for the workplace while also being designed with security and privacy features at its core.

Sounds intriguing…

But again, what does this mean for AWS customers?

The short answer is that it's an AI chatbot that provides employees with quick and relevant information to answer questions, generates content, and takes action using data and expertise from your company's information repositories, business data, code, and systems.

This is why I said it’s like ChatGPT for AWS, because it uses your AWS assets to answer your queries.

What’s really cool about this is that Amazon Q can personalize its interactions with users based on user identities, roles, and permissions. 

Put simply, it will use IAM to assess how to work with users.

Importantly, Amazon has said that it doesn't use business customers' content to train its underlying models. 

This is great to hear because you don’t want your proprietary data being used without your permission!

Amazon Q For Developers

To my mind, Amazon Q is not your run-of-the-mill LLM for answering questions about how far it is to the moon from Earth.

In fact, when it comes to cloud developers and IT professionals, this is the part you really need to get excited about!

Amazon Q has been trained on 17 years of AWS knowledge which means it’s been optimized to help you work within AWS.

I was immediately intrigued when I read this, and just like that, Amazon Q caught my attention! 

Think about this. If you've ever worked in the AWS cloud or worked on AWS applications, you've probably spent a fair amount of time reading docs and the well-architected manifesto!

Great reads, don’t get me wrong! In fact, I’d even go so far as to say that the AWS docs are some of the best out there.

But how cool will it be to ask Amazon Q how to do something rather than fumbling through the docs? Well, the future is now, as this is exactly what you can do with it!

This is massive to me, and I think it's going to revolutionize how we interact with AWS services.

I also appreciate how this lowers the barrier to entry for cloud professionals to implement best practices and solution patterns.

And that’s not to mention how it can help users learn to build in the AWS cloud quickly.

It's also great to see that Amazon Q is going to be integrated with their AI coding assistant, Amazon CodeWhisperer. 

This is very cool indeed, as it means you get access to a chat interface within your own development environment.

This is a massive leap forward for the CodeWhisperer and GitHub Copilot battle!

Big disclaimer: It's really early days for Amazon Q, as it’s only just been announced and is currently only available in preview mode within the AWS management console in US regions.

That said, I'm looking forward to playing with this more, and I’ll be sure to update you when I’ve learned more about its strengths, weaknesses, and any new features they might announce!

Generative AI With Amazon Bedrock & Titan

Before we wrap up, I also wanted to mention two more AI developments from Amazon in the form of Amazon Bedrock and Amazon Titan.

As I said, Amazon is leaning hard into generative artificial intelligence!

Amazon Bedrock

At first glance, my initial thoughts were: The Flintstones! But no, this is not that type of bedrock!

Amazon Bedrock is a powerful, fully managed, and serverless service designed to let you harness the capabilities of various cutting-edge generative AI models. 

What does this mean?

Put simply: Amazon Bedrock is an AWS service for building and scaling generative AI applications by accessing a range of large language models. 

You also get access to what Amazon calls foundation models (including Titan, which we’ll cover shortly).

Regarding the choice of model providers, Bedrock offers access to various options, including Claude from AI startup Anthropic and LLAMA 2 from Meta, among others.

This really stood out as they are two of my top choices for the best LLMs, and I love that you can build with various models inside the AWS ecosystem.

Hopefully, Amazon won’t mind me making another comparison with ChatGPT, but to me, this Amazon Bedrock is a lot like the developer's version of Create Your Own GPT.

If I’ve lost you there, you can currently Create Your Own GPT on the OpenAI platform, and there are even plans to release a marketplace to sell your own models next year.

Now, back to Bedrock. With this AWS service, you can do something similar but with far more control and a choice of which LLM model or foundation model to drive your AI.

This is really cool, and I can’t wait to take this for a test drive.

In terms of capabilities, Amazon Bedrock offers all of the usual features you’d associate with a modern AI tool.

This includes virtual assistants for natural language interaction, content generation, text and image search, text summarization, and image generation.

Again, not to make too many comparisons with ChatGPT, but if you can do it there, you can create your own AI with Bedrock to do the same. 

There’s even the option to personalize and adapt the underlying LLM models, which is perfect if you have a massive dataset you’d like to use to fine-tune your AI model.

This also links in with Amazon’s concept of Retrieval Augmented Generation, or RAG. 

The idea here is to enrich your model’s responses with up-to-date proprietary information from your company’s data sources.

In a nutshell, it ensures your AI model provides contextually relevant answers and accurate responses.

One thing’s for sure: there’s a lot to unpack with Bedrock, and I can’t wait to get started with it to build helpful tools for

I’ll also be sure to report back with my findings!

Titan Image Generation

One of the most popular use cases for generative AI is AI image generation, with tools like DALL-E, Midjourney, and Stable Diffusion being hugely popular.

Well, Amazon doesn’t want to feel left out!

Which is why they’ve also announced Amazon Titan. 

It’s actually part of Amazon Bedrock, and it’s one of the so-called Foundation Models (or FMs) that you can use within the Bedrock service.

Like any AI image generator, Amazon Titan can be used for advertising, e-commerce, media, and entertainment, as it can produce studio-quality, realistic images.

It can also enhance existing images, all using prompt engineering and natural language prompts.

Interestingly, Amazon Titan also applies an invisible watermark to all of its generated images. 

The idea behind this is to reduce the spread of disinformation by providing a way to identify AI-generated images.

I’ll be curious to see whether this divides opinion at all!

Now, while I’ve mentioned TItan specifically for image generation, it’s actually a diverse multi-modal FM for building AI tools for different functions with Bedrock.

You just need to choose between the various versions, which include:

  • Titan Image Generator: Rapid image generation using text prompts, image editing, and image variations.
  • Titan Text Express: Balances price and performance for text-related tasks, with support for retrieval augmented generation (RAG), summarization, code generation, and Q&A.
  • Titan Text Lite: Cost-effective and customizable model for text generation and summarization.
  • Titan Text Embeddings: Translates text into numerical representations, suitable for text retrieval, semantic similarity, and clustering.
  • Titan Multimodal Embeddings: Powers multimodal search and recommendation and includes support for fine-tuning.

As you can tell, Amazon Titan can be used for more than image generation! This extends to text generation, summarization, multimodal embeddings, and text embeddings. 

This means you can create various generative AI applications with Titan for content creation, image generation, text classification, or semantic search and recommendation. Very cool!

As I mentioned with Amazon Bedrock, I’m looking forward to testing this out to see how it stacks up against popular alternatives. 

I’ll also be interested in seeing how this compares to using the OpenAI API for creating an AI-powered chatbot or for semantic search. 

Wrapping Up

So there you have it! Amazon Q is definitely not another ChatGPT clone! 

In fact, I have a strong feeling that Amazon Q is going to irreversibly change how cloud developers approach their day-to-day work on the AWS platform.

Of course, it’s too early to tell whether Amazon Q will rival OpenAI for the title of best LLM, and given its business-facing design, this may not be very likely.

But, with the release of Amazon Bedrock and Amazon Titan, maybe you or I can create the next ChatGPT rival by creating our own generative AI service.

Plus, with the flexibility to use various LLMs or Foundation Models to power our AI tools, it’s a truly exciting time to be involved in AI development.

One thing is undeniable: AI is here to stay, and Amazon is investing heavily into generative AI with Amazon Q, Amazon Bedrock, and Amazon Titan.

So, if you use AWS, now is the perfect time to level up, whether that’s by using Amazon Q to be more productive or creating your own AI tools with Amazon Bedrock and Amazon Titan.

Whichever you choose to use, have fun, and let me know in the comments what you think about these new AI tools and services.

Are you new to the world of AI and eager to learn more? Check out:

Stanford's Artificial Intelligence Professional Program

Frequently Asked Questions

1. Does Amazon Have An AI Bot?

Yes, Amazon has recently released Amazon Q, which is a lot like a ChatGPT product for AWS users. This means it’s perfect if you’re a cloud developer who’s heavily invested in the Amazon Web Services platform.

2. Is Amazon Q Better Than ChatGPT?

While Amazon Q and ChatGPT have a lot in common, there are some key differences that make it hard to say whether one is better than the other. 

For example, ChatGPT is a general-purpose LLM tool, while Amazon Q is designed for business users within the AWS platform.

3. What’s The Difference Between Amazon Q & ChatGPT?

Amazon Q is an AI service that’s been designed for question-answering and query-based applications within the AWS ecosystem, while ChatGPT is a conversational AI model primarily focused on natural language understanding and generation for interactive conversations.

By Robert Johns

Technical Editor for | 15+ Years in Python, Java, SQL, C++, C#, JavaScript, Ruby, PHP, .NET, MATLAB, HTML & CSS, and more... 10+ Years in Networking, Cloud, APIs, Linux | 5+ Years in Data Science | 2x PhDs in Structural & Blast Engineering

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