Generative AI (GenAI) is redefining how organizations interact with data, automate workflows and enhance decision-making. AWS Bedrock provides a fully managed environment that simplifies the process of building, scaling and deploying GenAI-powered applications—all without worrying about model hosting or infrastructure management.
In this blog, I’ll share my hands-on experience using AWS Bedrock to create a custom AI agent with Guardrails, grounding and relevance, prompt engineering, and a knowledge base backed by Amazon S3.
Getting Started with AWS Bedrock
AWS Bedrock enables access to foundation models (FMs) from leading providers like Anthropic, AI21 Labs and Stability AI through a single API. This makes it easy for developers to experiment and integrate powerful LLMs into enterprise systems securely.
For this project, I used the Novalite Agent—a flexible, pre-built agent in Bedrock that allows experimentation in both chat and text-based interactions through the Chat/Text Playground. This interactive playground helped me tune responses, test prompts, and explore the model’s reasoning capabilities.
Implementing Guardrails for Responsible AI
Responsible AI is a key principle when deploying GenAI applications. AWS Bedrock provides Guardrails that ensure your agent behaves safely and ethically.
Here’s how I configured my agent’s guardrails:
- PII Detection and Masking: The agent automatically detects and blocks Personally Identifiable Information (PII) to prevent data leakage.
- Content Filtering: I set up context restrictions to block certain domains, such as medical advice or other sensitive topics.
- Custom Policy Enforcement: Specific rules ensure that responses stay compliant with organizational policies and data protection standards.
These guardrails make your agent enterprise-ready—safe, reliable, and aligned with compliance requirements.
Grounding and Relevance: Ensuring Trusted Responses
One of the major challenges with LLMs is hallucination—when models generate plausible but incorrect responses. To address this, I incorporated grounding and relevance techniques in my Bedrock agent:
Grounding: Ensures the model bases its responses on factual and verifiable data sources rather than just its internal training.
Relevance Filtering: Keeps responses contextually focused, discarding unrelated or speculative information.
This results in outputs that are more accurate, transparent, and trustworthy—critical for production-grade AI systems.
The Importance of Prompt Engineering
While models like those in AWS Bedrock are incredibly powerful, the quality of your prompts directly influences the quality of the model’s output. This is where Prompt Engineering becomes essential.
Prompt Engineering is the practice of designing and refining prompts that guide the model to produce the most relevant, coherent, and accurate responses.
A well-crafted prompt can:
- Clarify intent: Clearly define what the model should do, avoiding ambiguity.
- Provide context: Include background information or constraints to make outputs more precise.
- Control tone and style: Guide the model to respond in a professional, conversational, or technical tone as needed.
- Improve reliability: Reduce hallucinations and ensure the model stays on-topic.
In my experiments, I noticed how small prompt tweaks—like specifying format (“respond in bullet points”) or adding context (“use the S3 knowledge base as reference”)—dramatically improved the accuracy and structure of responses.
When combined with grounding and a knowledge base, prompt engineering acts as the bridge between human intent and model intelligence, helping you get consistently meaningful results from Bedrock.
Building a Knowledge Base in AWS Bedrock
A key part of my project was creating a Knowledge Base to provide my agent with contextual awareness and organization-specific data. Bedrock supports multiple types of knowledge bases, allowing flexible integration based on your data source.
Vector Store
- Build a fully customizable knowledge base with maximum flexibility.
- Specify the location of your data, select an embedding model, and Configure a vector store.
- Bedrock automatically stores and updates embeddings, enabling efficient semantic retrieval for relevant responses.
Structured Data Store
- Ideal for structured data such as databases or tables.
- Enables semantic search and retrieval within your existing systems.
- Allowing your agent to deliver accurate, data-driven answers.
Kendra GenAI Index
- Powered by Amazon Kendra, this option enables deep document understanding and search.
- Useful for retrieving context from unstructured documents such as PDFs, reports, and policies with high precision.
In my use case, I connected the agent to an S3-based knowledge base to provide access to domain-specific data. This allowed the agent to generate grounded, context-aware responses directly based on enterprise content.
Understanding Action Groups
While the knowledge base provides information, Action Groups define what the agent can do.
An Action Group in AWS Bedrock specifies a set of operations or functions your agent can perform, such as:
- Querying data from a knowledge base
- Calling APIs or triggering workflows
- Executing specific business logic
- Applying guardrails and filters
By modularizing these capabilities, Action Groups ensure that your AI agent operates within defined boundaries, remains auditable, and aligns with enterprise goals.
Enhancing Development with Amazon Q
To further enhance the GenAI development experience, AWS offers Amazon Q—an AI-powered assistant designed to help developers, data scientists and architects work more efficiently.
With Amazon Q, you can:
Use it as an extension in your code editor, such as VS Code, to get real-time code suggestions and explanations.
Integrate it with AWS Console or Bedrock workflows to assist with prompt optimization, model selection or debugging.
Quickly generate infrastructure-as-code templates, deployment scripts or Bedrock configurations using natural language.
In short, Amazon Q acts as a personal co-developer—accelerating your Bedrock projects and making the entire GenAI development lifecycle smoother and more intelligent.
Key Takeaways
Responsible AI Is Actionable: Guardrails make it easy to enforce safety and compliance in GenAI applications.
Prompt Engineering Drives Quality: The better your prompt, the better your model’s understanding, precision, and reliability.
Grounding Enhances Trust: Connecting your agent to verified data sources reduces hallucinations and improves user confidence.
Knowledge Base Adds Intelligence: Using S3, Vector Stores, or Kendra Indexes allows your AI to leverage both structured and unstructured enterprise data.
Action Groups Enable Control: They define what your AI agent can do, ensuring flexibility without compromising governance.
Amazon Q Accelerates Development: With Q integrated into your editor, you can experiment, refine, and deploy GenAI applications faster and smarter.
Conclusion
AWS Bedrock makes it remarkably easy to build intelligent, safe, and scalable GenAI applications. By combining the Novalite Agent, Guardrails, Grounding, Prompt Engineering, and an S3 Knowledge Base, I was able to create an AI assistant that not only understands context but also respects privacy and organizational constraints.
And with Amazon Q as a coding companion, the process becomes even more seamless—bridging the gap between creativity, compliance, and efficiency.
For teams exploring GenAI adoption, AWS Bedrock offers the perfect balance of innovation, control, and responsibility—a true enterprise-ready foundation for the next generation of AI-driven solutions.
If you have questions or want to share your experience, feel free to drop a comment or reach out to me at https://www.linkedin.com/in/shubham-kumar1807/
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