RAG Architectures

Introduction

Many people are now using AI tools to get fast answers to their questions. One of the smartest tools for this is called RAG, which stands for Retrieval-Augmented Generation. HYBot uses RAG to help you ask questions and get real answers based on your own documents. But there isn’t just one way to do RAG. There are many different methods, or “architectures,” that change how RAG works behind the scenes.

In this blog, we will explain RAG Architectures in HYBot in a way anyone can understand. Whether you’re an HR manager, a developer, or a team lead, you’ll learn what each type does, why it matters, and how HYBot uses them to help your company work smarter.

To try HYBot live, visit www.hyperict.fi

What Is RAG?

RAG stands for Retrieval-Augmented Generation.

Let’s break that down:

• Retrieval means finding pieces of text from your documents that are relevant to a question.
• Augmented means helping or improving the AI by using those pieces.
• Generation means the AI writes a full answer using the retrieved content.

So when you ask:

“What’s our company’s remote work policy?”

HYBot will:

1. Search your internal HR documents
2. Find a paragraph that talks about remote work
3. Use that real paragraph to build a clear and correct answer

This way, HYBot does not guess. It uses real information from your organization.

Why Different Architectures Exist

RAG is a smart system, but not every business needs the same type of smart. Some need speed. Others need security. Some need deep reasoning. That’s why there are multiple RAG Architectures in HYBot — each designed for a different use case.

Let’s go through them one by one.

1. Vanilla RAG (Simple RAG)

What it is:

This is the most basic type of RAG. The system finds the top few matching pieces of content and gives them to the AI to generate an answer.

How it works:

If you ask: “What is our dress code?”

HYBot finds three sentences from your HR document about clothing rules, and writes a clean answer using them.

Best for:

• Simple questions
• FAQs
• Quick lookups

Limitations:

• It doesn’t always include the full context
• May miss nearby important text

Vanilla RAG is fast, simple, and perfect for small datasets.

2. Hybrid RAG

What it is:

This architecture mixes keyword search and semantic (meaning-based) search. It gives better results by combining both.

How it works:

You search for “VPN access.” One document says “Virtual Private Network setup” and another says “VPN login.” The keyword search finds exact matches. The semantic search understands the meaning. Hybrid RAG uses both to get the most accurate answer.

Best for:

• Documents written in many styles
• Mixed data formats
• Cases where users may phrase things differently

Hybrid RAG gives flexibility. It covers more ground and avoids blind spots.

3. Multi-Vector RAG

What it is:

In this architecture, HYBot creates multiple vectors (representations) for each section of a document. Each vector focuses on a different part.

How it works:

Let’s say a document has a title, a table, and a paragraph. HYBot creates a separate vector for each. That way, if your question matches the table but not the paragraph, it still finds the answer.

Best for:

• Long documents
• Complex files with mixed formats
• Multi-topic pages

This architecture helps HYBot search deeply inside different layers of the content.

4. Hierarchical RAG

What it is:

This version of RAG works in steps. First, it finds a general topic. Then it goes deeper and looks for the most relevant detail inside it.

How it works:

You ask: “How do I file a complaint?”

HYBot first finds the document about employee policies. Then inside that, it zooms in on the complaint process.

Best for:

• Documents with headings and sections
• Step-by-step procedures
• Company handbooks or large policy files

Hierarchical RAG works like a smart librarian: first find the right book, then the right page, then the right line.

5. Agentic RAG

What it is:

This is one of the most advanced RAG architectures. The AI acts more like a human assistant. It can break your question into parts, plan steps, and gather info from different places.

How it works:

You ask: “How do I onboard a remote intern and make sure they have system access and an email account?”

HYBot sees that this is three tasks: onboarding, system access, and email setup. It looks in three places, builds an answer from all of them, and gives you a full step-by-step guide.

Best for:

• Complex multi-part questions
• Operational workflows
• Assistant-like tasks

Agentic RAG is like having an AI helper that thinks ahead.

6. Domain-Specific RAG

What it is:

This architecture uses AI that understands your specific field. That could be law, healthcare, finance, or software engineering.

How it works:

In healthcare, the word “discharge” means a patient going home. In legal documents, “discharge” means ending a responsibility. HYBot knows the difference when using domain-specific models.

Best for:

• Organizations with technical terms
• Compliance-heavy sectors
• Industries with specialized vocabulary

Domain-Specific RAG means HYBot speaks your language.

Why These Architectures Matter

Each of these RAG Architectures in HYBot gives different benefits:

• Vanilla is simple and fast
• Hybrid is flexible and smart
• Multi-Vector is deep and detailed
• Hierarchical is structured and focused
• Agentic is interactive and helpful
• Domain-Specific is accurate and specialized

HYBot is built to support all of these. It selects the right method depending on the question, the user, and the documents.

What Does This Mean for You?

You don’t need to know how each architecture works in detail. You just need to know that HYBot:

• Understands your questions
• Searches your real documents
• Respects access control
• Chooses the best method automatically
• Gives safe, accurate, explainable answers

This means better decisions, faster work, and fewer mistakes.

Real-World Scenarios

HR Manager:

“What’s our procedure for sick leave?”

HYBot uses Vanilla or Hierarchical RAG.

IT Team Lead:

“How do I reset MFA for a contractor?”

HYBot uses Hybrid or Agentic RAG.

Compliance Officer:

“Show me all clauses related to GDPR data retention.”

HYBot uses Multi-Vector or Domain-Specific RAG.

Project Manager:

“Can I onboard someone remotely and give them VPN access on day one?”

HYBot uses Agentic RAG to combine steps from different documents.

Conclusion

Understanding RAG Architectures in HYBot helps us see how modern AI can go beyond simple Q&A. Each RAG style brings a different level of intelligence to your documents. HYBot is designed to switch between them as needed, without you having to worry.

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