RAG is a fancy name for a search. But it's not just any search—it's a search powered by artificial intelligence that helps large language models (like ChatGPT) give accurate answers using your organization's private data.
Let's break down what RAG in AI actually means: Retrieval Augmented Generation. In plain English, it's about finding (retrieving) relevant information and using it to generate better answers.
Why should you care about RAG?
Imagine asking ChatGPT about your company's latest sales figures or customer feedback. It wouldn't have a clue because it doesn't have access to your private data. This is where RAG comes in — it helps AI systems answer questions using your organization's actual information.
What is RAG used for?
Companies use RAG to enhance customer support with accurate product information, power up-to-date knowledge bases, analyze data across multiple platforms, and maintain compliance with the latest regulations. What makes RAG particularly valuable is its ability to connect information from different sources and provide relevant, contextual answers while maintaining security standards. Instead of spending hours searching through various systems or waiting for reports, teams can get immediate, accurate insights from their organization's collective knowledge.
How does the RAG system work?
Traditionally, RAG systems work by first creating a complete copy of all your documents and organizing them in a database (called an index) for quick searching. When you ask a question, the system searches this index to find relevant information and uses AI to generate an answer. For example, Glean search indexes data first.
A newer approach is gaining traction: no-index RAG, pioneered by Qatalog. Instead of maintaining copies of your data, this method connects directly to your existing platforms in real time. When you ask a question, it searches across your live systems, retrieves relevant information on the spot, and uses AI to generate an answer using the latest data. After providing the response, it discards the processed content rather than storing it.
Why does it matter for your business?
The choice between these approaches has real implications:
1. Setup time and costs
- Indexed: Weeks of implementation, high storage costs
- No-index: Minutes to connect, no storage costs
2. Data security
- Indexed: Your sensitive data gets copied to another system
- No-index: Data stays in your existing systems, reducing data breach risks
3. Information accuracy
- Indexed: Only as current as your last update
- No-index:: Always up-to-date
4. Maintenance
- Indexed: Regular reindexing needed
- No-index: No maintenance required
Related: Real-Time RAG: A technical deep dive
How to get started with RAG?
One of the biggest advantages of choosing Qatalog's RAG search engine is its simplicity. Unlike building your own RAG system or implementing traditional indexed solutions, getting started takes days, not months, and requires no specialized AI expertise on your team.
Here's your quick start guide:
Day 1: Sign up
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Create your Qatalog account
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Choose your service tier based on usage needs
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Get immediate access to the RAG platform
Days 1-2: Connect your data
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Use Qatalog's one-click connectors for popular systems (Microsoft 365 Suite, Google Workspace, Salesforce, BigQuerry, Snowflake, Zendesk, etc.)
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Point to your existing databases or APIs
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Invite colleagues
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Data stays in your systems—No migration or copying required
Days 1-3: Start using RAG
Begin talking to your data immediately. Qatalog automatically handles:
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Document processing
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Retrieval optimization
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Response generation
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Security and permissions
Scale your implementation
As your needs grow, simply:
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Connect additional data sources
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Add more users through your admin dashboard
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Scale usage up or down as needed
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Enable new features with a click
That's it—really! Qatalog is easy to set up with a free trial, but we’re happy to help. Book a call to discuss your use case.
Advantages of choosing Qatalog as your RAG provider
1. Immediate time-to-value
Unlike building your own RAG system from scratch that can take months to deploy, RAG as a Service offers:
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Minimal infrastructure setup requirements
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Direct connections to your existing data sources
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Pre-built integration frameworks
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Rapid deployment capabilities
2. Cost-effective solution
When compared to building and maintaining an in-house RAG system, the service model provides:
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No upfront infrastructure costs
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Reduced ongoing maintenance needs
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Scalable pricing that grows with your usage
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Lower total cost of ownership
3. Enterprise-grade security
Security isn't an afterthought—it's built into the core of Qatalog’s service:
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Native permission inheritance ensures data access follows your existing protocols
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No data copying or indexing required
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Complete audit trails for all interactions
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Real-time access control management
4. Superior performance at scale
The service maintains high performance regardless of data volume:
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Real-time AI data processing capabilities
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Zero maintenance overhead
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Consistent performance at scale
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Advanced analytical features
What are the real-world applications of RAG?
1. Enterprise search
Challenge: Finding relevant information across multiple platforms and departments.
Traditional approach: Search each system separately, ask colleagues, and browse folders.
RAG solution: "Find all information about Project Phoenix from the last quarter."
Result: Complete overview showing:
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Related documents
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Email threads
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Meeting notes
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Slack discussions
2. Customer support
Challenge: Support teams need instant access to accurate product knowledge.
Traditional approach: Search through scattered documentation and ticket history.
RAG solution: "What are the recent solutions for the API authentication error?"
Result: Comprehensive support info combining:
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Latest documentation
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Recent ticket resolutions
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Product updates
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Policy changes
3. Knowledge management
Challenge: Making organizational knowledge accessible and actionable.
Traditional approach: Maintain wiki documentation and rely on subject matter experts.
RAG solution: "What do we know about our cloud infrastructure migration?"
Result: Dynamic knowledge synthesis:
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Project documentation
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Meeting discussions
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Technical decisions
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Implementation learnings
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Known issues and solutions
4. Business insights & analytics
Challenge: Extracting meaningful insights from vast amounts of business data kept in Excel files, PDF reports, and BI tools.
Traditional approach: Request analysis from the data team, wait for custom reports, manually combine information.
RAG solution: "How have our key metrics changed since the new product launch?"
Result: Instant analysis of:
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Performance trends
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Customer feedback
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Usage patterns
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Revenue impact
5. Employee support & HR
Challenge: Answering common questions about policies and procedures.
Traditional approach: Navigate the intranet, contact HR, and search policy documents.
RAG solution: "What's the current parental leave policy and recent changes?"
Result: Clear overview including:
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Current policy details
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Recent updates
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Related benefits
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Application process
6. Sales intelligence
Challenge: Accessing customer and deal information quickly.
Traditional approach: Check multiple systems and compile information manually.
RAG solution: "Give me a complete overview of our relationship with ABC Corp."
Result: Comprehensive summary of:
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Communication history
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Deal status
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Support tickets
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Contract details
7. Compliance and risk management
Challenge: Ensuring adherence to the latest regulations and policies.
Traditional approach: Manual audits and document reviews.
RAG solution: "Show me contracts affected by the new privacy regulation."
Result: Detailed compliance report:
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Affected documents
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Required updates
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Implementation status
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Change history
8. Project management
Challenge: Keeping track of project status and decisions across teams.
Traditional approach: Review multiple project tools and attend update meetings.
RAG solution: "What are the key decisions and updates from Project X this month?"
Result: Project overview showing:
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Key decisions
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Status updates
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Resource allocation
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Risk assessments
9. Research & development
Challenge: Leveraging existing research and avoiding duplicate work.
Traditional approach: Manual literature review, consult with team members.
RAG solution: "What work have we done on machine learning optimization?"
Result: Comprehensive research summary:
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Previous findings
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Current initiatives
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Team expertise
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Related patents
What is the difference between Gen AI and RAG?
The main difference is that generative AI (GenAI) makes educated guesses based on its training data, while Retrieval-Augmented Generation (RAG) enhances these guesses by looking up answers in your organization's real documents and data.
Generative AI is a predictive model that generates responses based on patterns in its training data, typically derived from internet-scale information. While it’s powerful, it has limitations:
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It cannot access private or real-time data
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Responses may sound convincing but can be inaccurate
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Its knowledge is fixed at the time of training
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It cannot verify sources
In contrast, Retrieval-Augmented Generation enhances GenAI by acting like your smart assistant with access to your actual documents and data. It can:
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Search through specific data repositories
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Retrieve relevant information
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Generate responses based on this data
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Provide citations for transparency
What’s the future of search?
RAG technology is transforming how organizations interact with their data. Whether you choose a traditional, indexed, or real-time approach depends on your specific needs, but one thing is clear: AI-powered search is becoming essential for businesses wanting to make the most of their information.
Key takeaway
While RAG might sound complex, at its core it's about making search smarter. The key is choosing an approach that aligns with your organization's needs for security, freshness of information, and ease of implementation.