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Building Generative AI-Powered .NET Applications with Amazon Bedrock: A Comprehensive Guide

The year is , and lemme tell ya, generative AI is blowin’ up the way we live and work. It’s like this super powerful tech that makes it easier (and cheaper!) than ever to build crazy-smart applications. We’re talkin’ apps that can level up your customer experiences and make everyone way more productive. This blog post is your intro to Amazon Bedrock, this fully managed service that lets you build and scale generative AI apps using somethin’ called “foundation models” (FMs). Yeah, it sounds kinda techy, but trust me, it’s cool. We’re gonna walk you through how to hook up Amazon Bedrock to your .NET apps, givin’ you the tools and know-how to, like, totally unlock the power of generative AI.

Heads up, tho, this is just part one in a series. Later on, we’re gonna deep dive into all sorts of use cases and explore next-level concepts like Retrieval-Augmented Generation (RAG), knowledge bases, and agents within Amazon Bedrock. By the time we’re done with ya, you’ll be buildin’ custom generative AI applications like it’s nothin’.

Using Amazon Bedrock with .NET Code

Okay, so there are two main ways to tap into Amazon Bedrock’s functionality within your .NET code:

  1. Amazon Bedrock API: This gives you, like, direct access to the service’s core API. Max control, max flexibility, you feel me?

  2. AWS SDK for .NET: This one’s way more chill for .NET developers. It simplifies everything and makes it all super intuitive. Plus, it handles all the boring stuff like authentication, retries, and timeouts. Honestly, using the AWS SDK is the move—it’s just way easier to use and makes development smoother.

Essential NuGet Packages for .NET Generative AI Development

Alright, check it: these NuGet packages are your bread and butter for buildin’ generative AI-powered .NET apps with Amazon Bedrock:

  • AWSSDK.Bedrock: This bad boy lets you manage, train, and deploy models. We’re talkin’ listin’ available FMs, gettin’ the deets on specific ones, and even creatin’ custom model jobs.

  • AWSSDK.BedrockRuntime: This one’s all about sendin’ inference requests to models hosted in Amazon Bedrock. Basically, it’s how you get your AI to actually do stuff.

  • AWSSDK.BedrockAgent: This helps you create and manage agents and knowledge bases within Amazon Bedrock. Think of it like buildin’ your AI a little brain to work with.

  • AWSSDK.BedrockAgentRuntime: This lets you actually talk to those agents and query the knowledge bases you set up.

Pro tip: If you’re lookin’ for the down-low on exactly what each API can do, hit up the Amazon Bedrock API Reference.

Building a .NET Application with Amazon Bedrock: A Step-by-Step Walkthrough

Hold your horses! Before we jump headfirst into coding, let’s talk money. You gotta check out Amazon Bedrock’s pricing page so you don’t get hit with any surprise charges, ya know? The Generative AI Application Builder on AWS Implementation Guide and the Amazon Bedrock Pricing page have some examples and breakdowns to give you an idea.


  • You’re gonna need an active AWS account.
  • And obviously, you gotta be kinda comfy with .NET development and Visual Studio.

Step: Configure Model Access in Amazon Bedrock

For this walkthrough, we’re gonna be usin’ the Anthropic Claude . model in Amazon Bedrock. This Claude . is a total rockstar—it’s a large language model (LLM) developed by Anthropic that can handle a ton of different tasks, like crazy-sophisticated dialogue, creative content creation, and followin’ detailed instructions. It’s the real deal.

  • First things first, open up the Amazon Bedrock console and go to the “Model Access” section in the left navigation pane.

  • Check if the Anthropic Claude . model is enabled. If it says “Available to request,” then go ahead and select “Manage model access,” choose “Anthropic, Claude,” and hit “Save.” Usually, you get access instantly. Pretty sweet, right?

  • Double-check that the “Access status” column for the model shows “Access granted.” You’re good to go!

Step: Set Up AWS Identity and Access Management (IAM) Permissions

Listen, you can’t just waltz into Amazon Bedrock willy-nilly. Your user or role needs the right IAM permissions. It’s like a VIP pass, y’know?

  • Open up the IAM console.

  • Go to “Policies” and search for the “AmazonBedrockFullAccess” policy.

  • Attach this policy to your user. Boom, you’re in!

Now, we’re keepin’ it simple here with the managed “AmazonBedrockFullAccess” policy, but in a real-world, production environment, you gotta use the principle of least privilege. Basically, don’t give anyone more access than they absolutely need. It’s just safer that way. The Amazon Bedrock documentation has some good examples of IAM policies for specific use cases, so check those out if you need ’em.

Step: Implement the Solution

Alright, time to get our hands dirty with some code!

  • Install NuGet Packages: First up, we gotta install the NuGet packages we need from the AWS SDK for .NET. We’re talkin’ AWSSDK.Bedrock and AWSSDK.BedrockRuntime. This is super easy to do through the NuGet package manager in Visual Studio.

    • Right-click on your project in the Solution Explorer and choose “Manage NuGet Packages.”

    • Search for “AWSSDK.Bedrock” and install both of those packages. Done!

    Or, if you’re a command-line kinda person, fire up the .NET Command Line Interface (CLI) and run these bad boys:

    dotnet add package AWSSDK.Bedrock
    dotnet add package AWSSDK.BedrockRuntime

  • AWSSDK.Bedrock: This package hooks you up with the `AmazonBedrockClient` class. This is your go-to for callin’ Amazon Bedrock management API actions, like `ListFoundationModels`. It’ll show you all the awesome foundation models you can use.

  • AWSSDK.BedrockRuntime: This one gives you the `AmazonBedrockRuntimeClient` class. Now, this is where the magic happens—it’s how you invoke those Amazon Bedrock models to make inferences and get responses. Basically, it’s how you get your AI to actually *do* somethin’ useful!

  • Initialize and Invoke:

    • Start by initializin’ an `AmazonBedrockRuntimeClient` object.

    • Then, create an `InvokeRequest` object.

    • Set the properties for your request, like the `modelId` (for example, “anthropic.claude-v3”) and the all-important `prompt`. This is where you tell the AI what you want it to do.

    • Finally, pass that request object to the `AmazonBedrockRuntimeClient.InvokeModelAsync` method. This kicks off the model inference process, and the AI gets to work!

Code Example: Invoking Anthropic Claude 3.x on Amazon Bedrock

Check this out—this code snippet shows you how to use the Anthropic Claude 3.x model to generate some text based on your prompt.

// … (previous code for setting up client and request)

InvokeModelRequest request = new InvokeModelRequest()
ContentType = “application/json”,
Accept = “application/json”,
ModelId = “anthropic.claude-v3”,
Body = new MemoryStream(
prompt = “Human: Explain how async/await work in .NET and provide a code example\n\nAssistant:”,
max_tokens_to_sample = 2000

// Call the InvokeModelAsync method
InvokeModelResponse response = await client.InvokeModelAsync(request);

if (response.HttpStatusCode == System.Net.HttpStatusCode.OK)
ClaudeBodyResponse? body = await JsonSerializer.DeserializeAsync(
new JsonSerializerOptions() { PropertyNameCaseInsensitive = true }
Console.WriteLine(“Something went wrong”);

See what’s goin’ on there? It sends the prompt “Explain how async/await work in .NET and provide a code example” to the Claude 3.x model. Then, it prints the response it gets back to the console. Pretty cool, huh?

Introducing the Sample App

Want to see all this stuff in action? We gotchu covered. Head over to the `dotnet-genai-samples` GitHub repository. It’s got a whole sample .NET application that demonstrates everything we’ve talked about and more. This app will show you:

  • A list of all the foundation models you can use, along with their details.

  • A text playground where you can mess around with different text models on Amazon Bedrock. It’s a great way to practice your prompt engineering skills and see what these models can do.

Pricing Considerations

Okay, real talk again—you gotta know how much this stuff costs before you go buildin’ a huge application, right? The good news is, Amazon Bedrock’s pricing is pretty flexible:

  • Inference:

    • On-Demand and Batch: You pay for the input/output token sizes, the AWS Region you’re usin’, and the specific model you choose.

    • Provisioned throughput: This lets you reserve model units to guarantee a certain level of performance.

  • Model customization, Model evaluation, and Guardrails: For the nitty-gritty on these, check out the Amazon Bedrock pricing page.


One last thing—don’t forget to clean up after yourself! Once you’re done with this tutorial, go ahead and remove access to any foundation models you don’t need anymore. It’ll save you some money in the long run. The Amazon Bedrock user guide has all the instructions you need.

Fine-Tuning Foundation Models: Tailoring AI to Your Needs

While foundation models are impressive out of the box, you can make them even more powerful by fine-tuning them on your own data. This is where Amazon Bedrock truly shines – it provides a streamlined approach to model customization without the hassle of managing infrastructure or complex training processes.

Why Fine-Tune?

Think of it like this: a foundation model is like a super-smart but generic student. They’ve learned a ton of general information, but they haven’t specialized in anything yet. Fine-tuning is like sending that student to a specialized school where they can focus on a specific field and become an expert in that area.

By fine-tuning a foundation model, you can:

  • Improve Accuracy: Make your AI even better at understanding your specific domain and generating more relevant responses.
  • Adapt to Your Brand Voice: Teach your AI to communicate in a way that aligns with your brand’s tone and style.
  • Handle Unique Tasks: Train your AI to perform tasks that are specific to your business or industry, even if they weren’t part of the model’s original training data.

How to Fine-Tune with Amazon Bedrock

Amazon Bedrock makes fine-tuning surprisingly straightforward. You don’t need to be a machine learning whiz to make it happen. Here’s the gist:

  1. Gather Your Training Data: This is crucial! The quality of your training data directly impacts the performance of your fine-tuned model. Make sure it’s relevant, accurate, and representative of the tasks you want your AI to perform.
  2. Prepare Your Data: Amazon Bedrock expects your data in a specific format, typically JSON Lines (.jsonl). Each line in the file represents a single data point, with the input and desired output clearly defined.
  3. Create a Training Job: Use the Amazon Bedrock console or API to create a training job. You’ll need to specify the foundation model you want to fine-tune, the location of your training data, and any hyperparameters you want to adjust.
  4. Monitor and Evaluate: Amazon Bedrock tracks the training progress and provides metrics to help you evaluate the performance of your fine-tuned model.
  5. Deploy and Use: Once you’re happy with the results, you can deploy your fine-tuned model and start using it in your .NET applications just like any other Amazon Bedrock model.


And there you have it – a crash course in building generative AI-powered .NET applications with Amazon Bedrock! You’ve learned how to access foundation models, send prompts, handle responses, and even fine-tune models to your liking. Now go forth and build amazing AI-powered applications that’ll blow everyone away! And hey, keep an eye out for the next post in this series, where we’ll dive even deeper into the exciting world of generative AI with Amazon Bedrock.