DEV Community: Vrajesh Jayswal

Building Software Development Artifact Generator Using Amazon Nova and Amazon Bedrock

Vrajesh Jayswal — Fri, 16 May 2025 18:12:35 +0000

Context:

Contribution from Amazon Community Builder to AI Engineering Month : Amazon Nova

📌 The Need for a Software Artifact Generator

To be successful in the cutthroat market of today, the modern software development process needs to be quick, reliable, and efficient in productivity. While working in Software industries for a long time, I have observed that the software developers often spend a huge time on documentation for the software which includes System requirement specification, HLD, LLD, Technical design & architecture, Data model etc. These tasks can become bottlenecks, especially when building MVPs, prototypes, or internal tools. They slow down innovation and introduce inconsistencies across teams. To provide solution to this problem, I built a serverless API using AWS Lambda, API Gateway, and Amazon Bedrock's Nova foundation model to generate software artifacts on demand.

This post shares the architecture and implementation steps including code to build this prompt-to-artifact API.

Why Amazon Nova on Bedrock?

Amazon Nova is a powerful LLM available through Amazon Bedrock. It stands out due to its:

High performance in software development tasks
Strong contextual understanding
Seamless integration with other AWS services
No need for managing infrastructure

Using Nova allowed me to focus on prompt engineering and orchestration rather than worrying about model deployment, scalability, or latency.

Architecture Overview

Components Used:
• Amazon API Gateway: Exposes the API endpoint to accept user prompts.
• AWS Lambda (Python): Handles request processing and integrates with Bedrock to invoke the Nova model.
• Amazon Bedrock (Nova model): Powers the natural language-to-artifact generation.

This architecture is:
• Fully serverless
• Scalable and cost-efficient
• Easy to extend with logging, authentication, and additional models

Prompt-to-Artifact Flow

User submits a prompt via a RESTful endpoint.
Lambda processes the prompt and sends it to Amazon Bedrock.
Nova generates the software artifact, such as:
- Software requirement specification
- High level design
- Low level design
- REST API boilerplate
- Configuration files (e.g., Dockerfile, serverless.yml)
Lambda returns the result in a structured JSON response. ________________________________________

Implementation Steps:

Step-1: Login into AWS console and open Amazon Bedrock. First of all you need to request access for particular Amazon Nova model which one you want to use. Go to the ‘Model catalog’ from left-side menu list, filter on ‘Amazon’ and then click on ‘Available to request’ for particular model. I have requested access to Nova Lite & Nova Micro and it has been granted.

Step-2: Create AWS Lambda function with following code. This function calls Amazon Bedrock API with particular Amazon Nova model id.

import json
import boto3

bedrock_client = boto3.client('bedrock-runtime', region_name='us-east-1')  # Adjust region if needed

def lambda_handler(event, context):
    try:
        body = json.loads(event["body"])
        print(body)
        prompt = body.get("prompt", "")
        print(prompt)

        model_input = {
            "messages": [
                {
                    "role": "user",
                    "content": [
                        {
                            "text": prompt
                        }
                    ]
                }
            ],
            "inferenceConfig": {
                "maxTokens": 2048,
                "temperature": 0.7,
                "topP": 0.9
            }
        }

        response = bedrock_client.invoke_model(
            modelId="amazon.nova-micro-v1:0",
            body=json.dumps(model_input),
            contentType="application/json",
            accept="application/json",
        )

        response_body = json.loads(response['body'].read())
        generated_text = response_body['output']['message']['content'][0]['text']

        return {
            "statusCode": 200,
            "headers": {"Content-Type": "application/json"},
            "body": json.dumps({"artifact": generated_text.strip()})
        }

    except Exception as e:
        return {
            "statusCode": 500,
            "body": json.dumps({"error": str(e)})
        }

2.1: Go to Lambda function screen and create new Lambda function from scratch.

2.2: create IAM role and give two permissions to it as shown in below screen-shot. Then assign this IAM role to the Lambda as execution role as per above screen-shot.

2.3: Deploy the Lambda function. Then go to the ‘Code’ section of Lambda function and copy paste above given code and re-deploy the function again.

Step-3: Create HTTP POST API in AWS API Gateway and integrate it with Lambda function.
3.1: Create new HTTP API and integrate Lambda function with it.

3.2: Configure route and HTTP method for the API.

3.3: Define stage for the API.

3.4: Deploy the API.

Step-4: Call API from postman passing appropriate prompt in API request body.

Sample Use Case

Prompt:
" Generate requirement specification for small ecommerce website. Give response in markdown format."

API Response:
{"artifact": "# Requirement Specification for Small E-commerce Website\n\n## 1. Introduction\nThis document outlines the requirements for a small e-commerce website that allows users to browse, purchase, and manage products online.\n\n## 2. Overall Description\n\n### 2.1 Purpose\nThe purpose of this e-commerce website is to provide a platform for customers to browse and purchase products online, manage their orders, and track their delivery.\n\n### 2.2 Scope\nThe scope includes the development of a user-friendly website that supports product listings, shopping cart functionality, user accounts, payment processing, and order management.\n\n### 2.3 Stakeholders\n- Customers: End-users who purchase products.\n- Admin: Users who manage the products, prices, and inventory.\n- Developers: Team responsible for the development and maintenance of the website.\n\n### 2.4 Assumptions\n- The website will be accessible via desktop and mobile devices.\n- Internet connectivity is required for accessing the website.\n- Payment gateways will be integrated for secure transactions.\n\n### 2.5 Dependencies\n- Payment gateway services (e.g., PayPal, Stripe).\n- Hosting services for website deployment.\n- Third-party APIs for inventory management and shipping.\n\n## 3. Functional Requirements\n\n### 3.1 User Registration and Authentication\n- FR1: Users must be able to register with an email and password.\n- FR2: Users must be able to log in with their credentials.\n- FR3: Users must be able to reset their password if forgotten.\n- FR4: Admin users must have access to manage user accounts.\n\n### 3.2 Product Management\n- FR5: Admin users must be able to add, update, and delete products.\n- FR6: Products must have details including name, description, price, and inventory count.\n- FR7: Products must be categorized by type.\n- FR8: Admin users must be able to upload product images.\n\n### 3.3 Product Catalog\n- FR9: Customers must be able to browse products by category.\n- FR10: Customers must be able to view product details including images, descriptions, and prices.\n- FR11: Customers must be able to sort and filter products by price, popularity, and ratings.\n\n### 3.4 Shopping Cart\n- FR12: Customers must be able to add products to their shopping cart.\n- FR13: Customers must be able to view and update their shopping cart.\n- FR14: Customers must be able to remove products from their shopping cart.\n\n### 3.5 Order Management\n- FR15: Customers must be able to proceed to checkout and complete their purchase.\n- FR16: Customers must be able to select shipping and payment methods.\n- FR17: Customers must be able to view their order history and details.\n- FR18: Admin users must be able to track order statuses and manage shipments.\n\n### 3.6 Payment Processing\n- FR19: The website must integrate with a payment gateway to process payments securely.\n- FR20: Customers must receive payment confirmation and order details via email.\n\n### 3.7 User Account Management\n- FR21: Users must be able to view and update their account information.\n- FR22: Users must be able to view their order history.\n\n## 4. Non-Functional Requirements\n\n### 4.1 Performance\n- NFR1: The website must load within 3 seconds under normal conditions.\n- NFR2: The website must handle at least 100 concurrent users without performance degradation.\n\n### 4.2 Security\n- NFR3: User data must be encrypted during transmission.\n- NFR4: The website must comply with PCI DSS standards for payment processing.\n- NFR5: The website must implement measures to prevent SQL injection, cross-site scripting (XSS), and other common web vulnerabilities.\n\n### 4.3 Usability\n- NFR6: The website must be accessible and usable on both desktop and mobile devices.\n- NFR7: The website must provide clear and intuitive navigation.\n\n### 4.4 Reliability\n- NFR8: The website must be available 99.9% of the time.\n- NFR9: The website must have a backup and recovery plan in place.\n\n### 4.5 Maintainability\n- NFR10: The website must be modular and well-documented to facilitate future updates and maintenance.\n\n## 5. Appendices\n\n### 5.1 Glossary\n- E-commerce: Electronic commerce, the buying and selling of goods or services using the internet.\n- PCI DSS: Payment Card Industry Data Security Standard, a set of security standards for organizations that handle branded cards.\n\n### 5.2 References\n- PCI DSS Standards\n- Web Accessibility Guidelines\n\n---\n\nThis document serves as a comprehensive guide for the development of the small e-commerce website, ensuring that all functional and non-functional requirements are met."}

Next Steps

You can create front-end and integrate this API so prompt can be passed through front-end to generate software artifacts.

Conclusion

Amazon Nova is a transformative model for developers. With just a prompt, it can accelerate software creation from hours to minutes. Combined with AWS Lambda and API Gateway, Nova becomes a powerful ally in building intelligent, prompt-driven developer tools.
I’m excited to continue building and sharing more tools like this—and grateful to be part of the Amazon Community Builders program where innovation meets opportunity.

Resources and References:

• Amazon Bedrock Documentation https://docs.aws.amazon.com/bedrock/
• AWS Lambda Python Guide
https://docs.aws.amazon.com/lambda/latest/dg/lambda-python.html
• API Gateway with Lambda Integration
https://docs.aws.amazon.com/apigateway/latest/developerguide/apigateway-integrate-with-lambda.html

How to Build a Smart Chatbot Using Amazon Bedrock, Knowledge Bases, and Your Own Documents

Vrajesh Jayswal — Sat, 19 Apr 2025 03:50:25 +0000

In the era of AI, building a chatbot that goes beyond generic answers and understands your private business documents is no longer a luxury — it’s a strategic advantage. In this blog, I will l walk you through how to build an intelligent chatbot using Amazon Bedrock, Knowledge Bases, and documents stored in an Amazon S3 bucket.

What Is Amazon Bedrock?
Amazon Bedrock is a fully managed service that makes foundation models (FMs) from providers like AWS, Anthropic, Cohere, and Meta available via API. It abstracts away the heavy lifting — no infrastructure to manage, just smart AI on tap.
With Knowledge Bases, Bedrock enables Retrieval-Augmented Generation (RAG) — meaning your chatbot can retrieve relevant content from your documents and generate human-like responses using FMs.

What We’re Building

We’ll create a chatbot that can:
• Answer questions using your private documents (PDFs, DOCX, TXT) stored in S3.
• Retrieve relevant snippets using semantic search.
• Generate accurate, conversational answers using a foundation model.
This is ideal for customer support, internal documentation, onboarding, and more.

Step 1: Store Your Documents in Amazon S3
Start by uploading all the documents you want your chatbot to learn from into an S3 bucket.
Make sure the documents are well-organized and stored in a private bucket.
I created new bucket with name ‘my-private-documents101’ so my S3 bucket URI is : s3://my-private-documents101/

Step 2: Create a Knowledge Base in Bedrock
Navigate to the Amazon Bedrock console and go to the Knowledge Bases section.
2.1. Create a New Knowledge Base with Vector store

• Give it a name (e.g., my-documents-kb)
• Choose Amazon S3 as the data source

• Provide the URI to your bucket or folder (e.g., s3:// my-private-documents101 /) created in Step#1.

• Attach an IAM role that has read access to above bucket.

2.2. Configure Embedding and Vector Store

• Choose an embedding model like Titan Embeddings.

• Select a vector store such as Amazon OpenSearch, Pinecone, or Redis.

• Bedrock will create the vector index and connect it to your documents.

2.3. Ingest the Documents
Hit "Ingest", and Bedrock will process your files, breaking them into chunks, embedding them, and storing them in the vector database.

Step 3: Build the Chatbot Using Bedrock’s RetrieveAndGenerate API
Once the Knowledge Base is ready, use Bedrock’s API to build your chatbot.
Here’s an example in Python:
import boto3
bedrock = boto3.client('bedrock-agent-runtime')
response = bedrock.retrieve_and_generate(
input={'text': "What is our leave policy for fulltime employees?"},
knowledgeBaseId=,
retrievalConfiguration={
'vectorSearchConfiguration': {'numberOfResults': 3}
}
)
print(response['output']['text'])

Above function:

Takes the user’s question
Retrieves top-matching document chunks
Sends them to a foundation model
Returns a natural language answer You can wrap this logic inside a web app, Slack bot, or API endpoint.

Step 4: Secure Your Setup
Security matters — especially with private documents.
• Ensure your S3 bucket is private.
• Use IAM roles to control access to Bedrock and S3.
• Monitor activity using AWS CloudTrail or CloudWatch.

Optional Step: Add a Chat UI
You can pair your chatbot with a user interface using:
• Amazon Lex – for voice/chat interfaces
• React + AWS Amplify – to build a web UI
• API Gateway + Lambda – to expose your chatbot as a RESTful API
With just a little front-end work, you will have a sleek and smart chatbot that feels custom-made.

Conclusion:
Amazon Bedrock + Knowledge Bases + your private S3 documents = a chatbot that actually knows your business. Whether it's employee onboarding, customer support, or internal search, you now have the power to build a truly intelligent assistant

Generative AI - A Beginner’s Guide with AWS

Vrajesh Jayswal — Sat, 29 Mar 2025 05:33:44 +0000

Introduction

Generative AI is reshaping how businesses and individuals create digital content. Generative AI can create new content, including text, images, music, and even code. With AWS providing a comprehensive suite of AI-powered tools, users can easily build, deploy, and scale Generative AI solutions. Whether you're a developer, business owner, or AI enthusiast, AWS makes it easier than ever to tap into the power of Generative AI.
In this guide, we'll explore the fundamentals of Generative AI and how AWS empowers users to harness its potential.

Understanding Generative AI

Generative AI refers to models that can generate new data based on patterns learned from existing datasets. These models use deep learning techniques such as Generative Adversarial Networks (GANs) and Transformers to produce high-quality outputs.
Some common applications of Generative AI include:

Text Generation: Chatbots, content creation, summarization
Image Generation: AI-generated art, design, deepfake technology
Code Generation: AI-assisted programming
Music Composition: Creating original musical pieces

How AWS Supports Generative AI

AWS offers a range of AI and machine learning services that simplify the development and deployment of Generative AI models. Some of the key AWS services include:

1. Amazon Bedrock
Amazon Bedrock is a fully managed service that allows users to build and scale Generative AI applications using foundation models (FMs). It provides access to pre-trained AI models from leading providers, enabling quick integration into business workflows.

2. Amazon SageMaker
AWS SageMaker provides a comprehensive environment for training, deploying, and fine-tuning machine learning models. Developers can use SageMaker to create custom Generative AI applications while leveraging AWS’s scalable infrastructure.

3. AWS Lambda & API Gateway
For serverless AI applications, AWS Lambda can run AI model inference without managing infrastructure, while API Gateway allows seamless integration with web and mobile applications.

4. AWS CodeWhisperer
This AI-powered tool helps developers by suggesting code completions and snippets, making programming more efficient.

One of the most common and easy use case for Gen AI is Building a Chatbot with AWS
Use Case: Building a Chatbot with AWS
A simple example of Generative AI in action is creating an AI-powered chatbot using Amazon Bedrock.

Steps to Build a Chatbot with AWS
Step 1: Choose a Foundation Model in Amazon Bedrock

Navigate to the AWS Console and access Amazon Bedrock.
Select a Foundation Model (FM) such as Amazon, Meta, or any of your choice. Request access to particular model before using it.
Configure the model with necessary parameters for chatbot responses.

Step 2: Integrate with AWS Lambda for Real-time Responses

Create an AWS Lambda function in the AWS Management Console.
Write a function in Python to process chatbot requests and interact with the selected Foundation Model.

Step 3: Deploy via API Gateway for Web or Mobile Access

Set up an API Gateway to expose your Lambda function as a REST API.
Configure methods (GET/POST) to receive user input and send chatbot responses.
Deploy the API and obtain the endpoint URL for integration with web or mobile applications.

Step 4: Integrate with Frontend (Optional)

Create a simple frontend using React, Vue.js, or Angular to connect with your chatbot API.
Use WebSockets or Polling for real-time interactions.
Enhance the UI with interactive chat features.

Enhancement to Chatbot:

You can use Amazon Bedrock Knowledge Bases to provide contextual information from your various private data-sources so that the chat-bot can deliver more relevant and customized responses as per your need.

My Next Blog:

I will provide details steps with screen shot about how to create Chat bot using Amazon Bedrock with Knowledge bases and private documents from S3 bucket. Then how to expose API integrating the chat bot so it can be integrated with front-end or mobile application calling the API.