Domain Driven Design in Vector Databases

In-Short

CaveatWisdom

Caveat: We’ve all been swept up by the magic of Generative AI. Add a Retrieval-Augmented Generation (RAG) pipeline with Vector Databases on top, and your AI system suddenly sounds smarter—context-aware, grounded, and business-specific. But here’s the trap: Just bolting on a vector database doesn’t mean your AI is contextually intelligent. In fact, if your RAG database is a chaotic soup of embeddings from every business unit imaginable, you may have just created a slightly faster way to serve wrong answers—with more confidence.

Wisdom: Design for Domains, Not Just for Data

Enter Domain-Driven Design (DDD)—a principle born in software engineering but incredibly relevant for AI systems grounded in real-world business knowledge. At its core, DDD is about aligning your software (or in this case, your data and AI infrastructure) with the bounded contexts of your business.

 In-Detail

It’s easy to get caught up in the hype surrounding Generative AI. And when you add a Retrieval-Augmented Generation (RAG) pipeline, your AI system seems to get even better. It becomes more context-aware, grounded in facts, and tailored to your business. However, there’s a common pitfall: Simply adding a vector database won’t automatically make your AI contextually intelligent. If your RAG database is a disorganized mess of embeddings from every department, you might just be building a more efficient way to confidently provide incorrect information.

Take a banking AI agent that retrieves documents from a single, flat embedding index. A customer asks a question about a credit card limit. Instead of fetching documents from the “Credit Cards” context, it retrieves information about “Savings Account limits”—because, well, the phrase “limit” showed up in both. Wrong answers, hallucinated actions, and broken trust follow.

The caveat is simple but dangerous: RAG falls apart when the database doesn’t understand your business domains. If embeddings across domains like Loans, Credit Cards, and Deposits live in the same pot, you’re just asking for retrieval mismatches. The retrieval engine doesn’t know the difference—it just calculates similarity. If your data isn’t organized by domain, your AI will get confused. Just like a human would.

 Domain-Driven Design: Tailoring AI to Your Business Realities

Domain-Driven Design (DDD), a concept originating in software engineering, is highly applicable to building AI systems that are deeply rooted in real-world business understanding. Essentially, DDD focuses on synchronizing your software—or in this instance, your data and AI infrastructure—with the specific, well-defined areas (or “bounded contexts”) of your business operations. This ensures your AI isn’t just intelligent in a general sense, but intelligently aligned with how your business actually functions.

A bounded context is a conceptual boundary within which a particular domain model is defined and applicable. In banking, “Loans” and “Deposits” don’t just differ in terms of data—they speak different business languages. The term “balance” means very different things depending on whether you’re talking about a savings account or a credit card. DDD respects that difference. So should your RAG system.

By applying DDD to Vector databases, we’re essentially saying: Don’t just index everything into one monolithic vector table. Model your domains separately and build your RAG stack to reflect that.

Case Study: Banking AI Agent with Split Brain That Works

Let’s build this out with Amazon Bedrock Agents, Aurora PostgreSQL Serverless with pgvector, and Knowledge Bases.

Imagine a customer-facing banking AI assistant that operates across three major domains:

• Credit Cards & Loans
• Deposits & Savings
• General Banking Policies

In the above figure banking functions like Application Processing, Interest Calculation, Debt Collection and Statement Generation could similar in their names and wording but they will have a different meaning as per the context of the domain.

Aurora PostgreSQL Serverless as Vector Store (With Domain Separation)

Instead of using a single embeddings table, we model our bounded contexts as individual vector tables:

CREATE TABLE embeddings_credit_loans (
    id UUID PRIMARY KEY,
    chunk TEXT,
    embedding VECTOR(1536),
    metadata JSONB,
    created_at TIMESTAMP DEFAULT now()
);

CREATE TABLE embeddings_deposits_savings (
    id UUID PRIMARY KEY,
    chunk TEXT,
    embedding VECTOR(1536),
    metadata JSONB,
    created_at TIMESTAMP DEFAULT now()
);

CREATE TABLE embeddings_general (
    id UUID PRIMARY KEY,
    chunk TEXT,
    embedding VECTOR(1536),
    metadata JSONB,
    created_at TIMESTAMP DEFAULT now()
);

Now, the AI agent’s RAG pipeline includes a context-aware retrieval layer. When the customer asks, “What is my pre-approved credit limit?”, the retrieval layer routes the query to the embeddings_credit_loans table. The AI doesn’t waste cycles comparing cosine distances with a deposit policy document.

In practice, this could be implemented as:

def route_query_to_context(user_query):
     if "credit" in user_query or "loan" in user_query:
         return "embeddings_credit_loans"
    elif "savings" in user_query or "deposit" in user_query:
        return "embeddings_deposits_savings"
    else:
       return "embeddings_general"
 

Of course, this can (and should) evolve into a more nuanced classifier or intent router—but the principle stands: domain comes first, retrieval follows.

 Plugging into Agents on Amazon Bedrock

Here’s where it gets powerful. Agents in Amazon Bedrock are designed to orchestrate multi-step tasks by combining foundation models with structured tools and knowledge sources. Each agent can have its own configuration, API schema, and now—thanks to RAG—its own Knowledge Base.

With DDD in mind, you could design your banking assistant as multiple agents or one master agent with domain-specific capabilities.

Agent Capabilities Example

When a user interacts with the main AI assistant on your banking app, the Bedrock Agent router figures out which domain the query belongs to, and forwards the request to the relevant sub-agent, which uses the correct vector index.

This makes your agent contextually grounded, auditable, and modular—a dream for banks with compliance checkboxes.

Knowledge Bases with Aurora: Bring Your Own Vector Store

Amazon Bedrock recently introduced Bring Your Own Vector Store (BYOVS) support in Knowledge Bases. And yes, Aurora PostgreSQL with pgvector is one of the supported configurations.

So you can link each bounded context’s table as a separate Knowledge Base in Bedrock:

knowledge_bases:

  – id: credit_loan_kb

    vector_store: aurora_pg

    table_name: embeddings_credit_loans

  – id: savings_kb

    vector_store: aurora_pg

    table_name: embeddings_deposits_savings

Now, when your Loans Agent is triggered, it automatically queries the credit_loan_kb. No guesswork, no cross-domain leakage. Everything’s tightly scoped, as per DDD principles.

And It Scales Without You Thinking About It

Aurora Serverless v2 means you don’t have to overprovision for bursty vector searches. Your AI assistant can scale from 10 queries/day to 10,000/day without hitting a bottleneck. Latency stays low. Costs stay predictable. You pay for what you use—not for a cluster sitting idle. Combine that with Bedrock’s serverless agent orchestration and you’ve got a truly elastic GenAI stack that aligns with how your business actually works.

Beyond Tech: Organizational Clarity

This approach also has a second-order benefit: clarity in ownership. Each embedding table can be owned by the respective business unit. The Loans team curates and governs their vector store. The Deposits team does the same for theirs. No more spaghetti governance or confused compliance reviews.

Suddenly, your AI architecture starts to look like your org chart—and that’s a good thing.

Closing Thought: Your RAG Isn’t Smarter Than Its Design

You can fine-tune models, optimize cosine distances, and add caching layers—but if your RAG system isn’t built with domain boundaries in mind, you’re setting up your AI agent to retrieve wrong answers faster. That’s not intelligence. That’s well-dressed ignorance.

So the next time you’re designing a RAG pipeline, step back and ask: What are my domains? What are the bounded contexts?

The wisdom here is simple and timeless:

Design like you understand the business — not just the tech.