How to Build with GenAI on AWS: A Quick-Start Guide

Between the hype and the how-to, there’s a gap. You’ve heard what Generative AI can do—create content, transform customer experiences, automate workflows—but translating that into something real for your business? That’s another story. A lot of the people we talk to—business owners, innovation leads, digital teams—know Generative AI has potential, but they’re not sure how to get started. They're asking:

• “Where do we even start?”
• “How do we use our own data without building everything from scratch?”
• “What if it costs a fortune or we choose the wrong tools?”

This article is designed to help you cut through that uncertainty. Whether you're experimenting or already have a use case in mind, we’ll walk you through how to build with Generative AI (or “GenAI”) on AWS, step-by-step. And we’ll show how others have done it using services like Amazon Bedrock, OpenSearch, and AWS Lambda—so you can move forward without reinventing the wheel.

Step 1: Understand What You Can Build—and With What

Before jumping into code or architecture diagrams, it’s important to understand the landscape. GenAI isn’t a single tool or magic button—it’s a set of capabilities you can combine in powerful ways, depending on your use case.

AWS provides a wide array of building blocks for creating GenAI solutions. Here are the key services you should be aware of:

• Amazon Bedrock – API-based access to top foundation models from Anthropic (Claude), Meta (Llama 2), Mistral, and Amazon Titan. Fully managed, no need to host models yourself.
• Amazon Nova – AWS’s latest GenAI assistant designed to help users write, troubleshoot, and query using natural language across AWS services. Nova can serve as a development accelerant and user-facing support tool.
• Amazon S3 – Scalable storage for documents, PDFs, product data, or any files you want your GenAI solution to reference or extract from.
• Amazon OpenSearch Service – Enables vector-based similarity search—essential when building intelligent retrieval systems like chatbots or semantic search interfaces.
• AWS Lambda – Serverless functions that trigger workflows, sync data, or transform inputs and outputs between services.
• Amazon Comprehend / AWS Reckognition / Amazon Transcribe – Managed machine learning services for tasks like sentiment analysis, entity recognition, image and video labelling, or speech-to-text, with minimal setup and no model training required.
• Amazon SageMaker – A full-featured suite for training, deploying, and fine-tuning custom machine learning models when you need full control or have highly specialised requirements.

Understanding these services helps you identify the right tool for the right job. Most successful projects start small—just Bedrock and Lambda, perhaps—and evolve from there. Once you know what’s in your toolbox, you can start solving real problems.

Step 2: Choose a Real Use Case

Jumping into GenAI without a defined goal is like setting off on a road trip with no map. The best place to begin is by identifying a real problem to solve. That might be generating content, improving search, summarising documents, or extracting insights from messy data.

Let’s look at two very different but very real examples:

Agrovia: From Farm Data to Customer-Ready Content

Agrovia, a Swedish startup, helps small food producers market their products. Many of their customers lacked time and resources to create high-quality content. Using AWS, Agrovia built a solution that:

• Used a custom pipeline with AWS Lambda and Step Functions to process structured data from spreadsheets and web sources
• Integrated with Amazon Bedrock to generate natural-language product descriptions
• Delivered ready-to-use marketing copy in seconds, significantly reducing manual effort

The net result was that small producers could publish engaging content more quickly, helping them reach customers faster without needing technical expertise or creative support in-house.

Greek National Documentation Centre (EKT)

Meanwhile, Greece’s National Documentation Center (EKT) had a vast collection of scientific content that was hard to access using keyword search. They needed a multilingual, intelligent search experience for researchers. Partnering with us, they built a solution that:

• Stored documents in Amazon S3
• Indexed them using OpenSearch with semantic vector embeddings
• Used Amazon Bedrock (Claude + Titan) to generate context-aware responses
• Delivered results with citations, supporting academic trust

The result was a highly scalable, researcher-friendly platform that made Greek scientific content more accessible on an international level—without compromising academic rigour or data integrity.

At this point, it’s time to start putting things into practice. Ideally, you’ll already have a use case in mind—but don’t worry if not. The next step is about trying things out and seeing what’s possible by building a small, simple application.

Step 3: Build a Simple GenAI App (Really)

Once you’ve identified a use case, it’s time to build something for real. You don’t need a full application yet—a functional proof of concept is a great way to test feasibility and get buy-in.

For example, you could build a lightweight tool that:

1. Accepts a prompt from a user (e.g. “Summarise this meeting transcript”)
2. Uses AWS Lambda to send the request to Amazon Bedrock
3. Receives a response from a foundation model like Claude 3.7
4. Displays it via a simple interface (e.g. Slack bot, internal web app, CLI)

This kind of lightweight prototype is a great first step because it demonstrates real value quickly while keeping the technical complexity low and well within the range of a beginner:

• It gives users a feel for what GenAI can do
• You can iterate on prompts quickly
• It introduces core services without deep infrastructure overhead

Once you’ve seen this working in a contained environment, you’ll be in a strong position to decide how to extend it—whether by incorporating your own data, adding automation, or designing for scale.

Step 4: Add Your Data with Retrieval-Augmented Generation (RAG)

Once you've proven that your GenAI app can generate useful responses, the next step is to make those responses more relevant and reliable by incorporating your own data. That’s where Retrieval-Augmented Generation (RAG) comes in.

RAG combines semantic search with language models. Instead of trying to fine-tune a model on your data, you:

• Store your data in a system like Amazon S3
• Create vector embeddings using a model (e.g. Amazon Titan)
• Index and retrieve relevant documents with OpenSearch
• Pass the retrieved snippets into your GenAI prompt for a more accurate, contextual response

This is exactly what the Greek National Document Centre did to overcome their search and accessibility challenges. Their platform was designed from the ground up to make large volumes of scientific content more discoverable and usable by researchers, using the full range of AWS’s GenAI and search capabilities:

• Ingested 25,000+ documents
• Generated embeddings for semantic indexing
• Enabled real-time multilingual querying
• Returned answers with citations, building trust in outputs

By grounding the model’s responses in your own knowledge base, you create something far more useful than generic AI responses—while also avoiding the need to retrain a model from scratch. Now that you’ve grounded your GenAI app in your own data, the next step is to make sure it runs smoothly, securely, and reliably.

Step 5: Secure, Monitor, Automate

As your solution matures, you’ll need to consider security, observability, and automation. The good news? These are native strengths of the AWS platform.

Here’s what to prioritise:

• IAM – Lock down access to Bedrock APIs and data sources
• CloudWatch – Monitor logs, latency, and errors from Bedrock and Lambda
• Automation with Lambda – Keep your system up to date—like EKT’s automatic tagging and syncing for new scientific documents

By building with security and operational visibility from the outset, you’ll avoid technical debt and future headaches. With the foundations in place, you can now start layering on more intelligence.

Step 6: Add NLP or Deeper Analysis (Optional)

With your core infrastructure established and running reliably, you can now shift focus toward enhancing your application’s intelligence by improving how it interprets your data. Many tasks—like summarisation, keyword extraction, or sentiment analysis—can now be handled directly through prompt engineering using models such as Claude via Amazon Bedrock. This approach often provides greater flexibility and reduces the need for additional services.

However, if your project requires predictable costs, rapid processing of structured tasks, or plug-and-play capabilities, services like Amazon Comprehend may still be worth considering. The best path depends on your workload and budget profile.

In Agrovia’s case, the team built a lightweight data processing layer using AWS Lambda and Step Functions to extract relevant product details from spreadsheets and online sources. This structured data was then passed to Bedrock, enabling the generation of high-quality, consistent marketing content that reflected each producer’s unique offerings.

Whether you use built-in NLP services or a powerful foundation model with carefully crafted prompts, the principle remains the same: preparing your inputs thoughtfully will make your outputs smarter, more consistent, and better aligned with your brand.

Step 7: Optimise for Cost and Scale

Finally, even the smartest solution needs to make sense operationally. GenAI can be resource-intensive, but AWS provides ways to keep things lean and responsive. Here are some practical strategies to help you balance performance and cost:

• Use provisioned throughput if your production GenAI application outgrows the regular throughput, as it can be costly if overused
• Set up model aliases to safely evolve prompt workflows
• Use serverless tools like Lambda and managed services like Bedrock to avoid infrastructure overhead
• Store data efficiently in S3, and let OpenSearch scale indexing as your dataset grows

EKT’s system shows how this can work in practice: a cloud-native, serverless design allowed them to expand content coverage and language support while keeping operational costs in check. Once your app is stable, scalable, and secure, you’re ready to go beyond the proof of concept.

Wrap-Up: From First Step to Full Build

You’ve got the tools at your fingertips, and the success stories to prove they work. The opportunity to create something genuinely impactful—whether for your team, your customers, or your entire organisation—is right there, waiting to be acted on.

With AWS GenAI services and a well-defined business challenge, you can turn ideas into working solutions faster than you might expect. Start small with a proof of concept. When you're ready, bring in your own data. Then scale it securely, monitor performance, and let your application evolve with your needs.

Whether you're building product content like Agrovia or enabling intelligent research like EKT (or whatever else it is that floats your boat), GenAI on AWS makes it possible to move quickly and build smart.

Further Reading

If you’re curious to go deeper, here are a few AWS resources we recommend. Good luck with your GenAI journey!

• Amazon Comprehend – NLP on AWS
• Amazon OpenSearch Service Documentation
• Amazon Bedrock FAQs – When to use Bedrock vs SageMaker
• Build multimodal search with Amazon OpenSearch Service
• Build scalable and serverless RAG workflows with a vector engine for Amazon OpenSearch Serverless and Amazon Bedrock Claude models