Retrieval-Augmented Generation (RAG) is a powerful AI tool that combines the strengths of both data retrieval and content generation, providing businesses with a way to make quicker, more informed decisions. RAG pulls from vast datasets to find relevant information and then generates concise, accurate insights, allowing decision-makers to act faster. With its ability to analyse data from multiple sources in real-time, RAG is becoming the holy grail for businesses looking to streamline operations and boost efficiency.
We recently tested Google Notebook LM’s RAG functionality by sifting through 65 pages of Waverley Council rules to find the guidelines for how pergolas should be used. The AI not only parsed through this massive document quickly but also provided a clear, concise summary of the relevant sections, helping me easily retrieve the necessary information without manually reading through pages of content.
This is a perfect example of how RAG can be the ultimate tool for your entire business, putting data-driven decision-making at your fingertips. Imagine a large automotive company trying to decide whether to invest in a new piece of machinery. By crunching data from financial reports, market trends, and operational costs through Google Notebook LM, the company can receive not only a final decision but also a confidence percentage—let’s say, over 90% likelihood that it’s a sound investment. This ability to make informed decisions quickly is what makes RAG so valuable for businesses in any sector.
Top 10 Common Inputs to a RAG for Medium-Sized Businesses
Here’s a checklist of some of the most common data inputs a medium-sized business could use to harness the power of RAG:
- Payroll Data
- Employee Handbook
- Customer CRM
- Sales Data & Reports
- Inventory Management Data
- Financial Statements
- Marketing Campaign Data
- Customer Feedback & Reviews
- Product Performance Metrics
- Market Trends & Competitive Analysis
These inputs help businesses like yours consolidate and analyse data from various sources, allowing you to make well-informed decisions in real time.
If you’d like to learn more about how AI can help you grow, consider attending a Nimbull AI Training Day or reach out for AI Consulting services.
Introduction
Retrieval-Augmented Generation (RAG) is an advanced AI architecture (in the category of enterprise AI knowledge systems and decision intelligence) that combines information retrieval with AI-generated reasoning and summarisation. Rather than relying solely on a model’s training data, RAG systems actively retrieve relevant information from external sources—documents, databases, knowledge bases—and then generate accurate, context-aware responses based on that retrieved data.
This approach dramatically improves accuracy, transparency, and usefulness in real-world business environments. By grounding AI outputs in verified internal and external data, RAG enables faster decision-making, reduces hallucinations, and allows organisations to unlock value from large, unstructured datasets that would otherwise remain difficult to analyse at scale.
Competitor Comparison
Here’s how RAG-based systems compare with other AI approaches and tools:
| Tool / Approach | How It Compares to RAG |
|---|---|
| Traditional LLMs | Generate responses based on training data only. RAG enhances reliability by pulling live, source-backed information. |
| Enterprise Search | Retrieves documents but does not reason or summarise. RAG adds AI-generated insights and explanations. |
| BI Dashboards | Excellent for structured data but limited for PDFs, policies, and text-heavy documents. RAG excels with unstructured data. |
| Knowledge Bases | Require manual upkeep and navigation. RAG dynamically retrieves and explains information on demand. |
| Google NotebookLM | A practical implementation of RAG that allows users to upload documents and query them conversationally. |
In short, RAG bridges the gap between search, analysis, and decision support, making it significantly more powerful than standalone AI or traditional data tools.
Primary Users
The primary users of RAG systems include:
- Medium to large businesses managing extensive internal documentation.
- Legal, compliance, and regulatory teams analysing policies and legislation.
- Executives and managers needing fast, evidence-backed decision support.
- Operations and finance teams reviewing reports, forecasts, and performance data.
- Knowledge workers who regularly search across multiple data sources.
Pricing & User Base
At the time of writing:
- RAG is not a single product but an AI architecture implemented across multiple platforms and enterprise solutions.
- Pricing depends on the underlying tools used (e.g. AI models, vector databases, cloud infrastructure).
- Platforms like Google NotebookLM provide early-access or freemium-style testing environments for RAG-based workflows.
Difficulty Level
RAG is best categorised as Medium to Advanced, depending on implementation.
- End users can interact with RAG systems easily through chat-style interfaces.
- Technical setup (data ingestion, embeddings, retrieval logic) requires AI or engineering expertise.
- Once deployed, RAG significantly reduces manual research and analysis effort for non-technical staff.
For businesses, the value often outweighs the initial setup complexity.
Use Case Example
Task:
- Upload a 65-page council policy document into a RAG-enabled system (e.g. NotebookLM).
- Ask a direct question: “What are the rules governing pergola usage?”
- The system retrieves only the relevant sections of the document.
- AI generates a concise, plain-English summary of the applicable rules.
- Follow-up questions refine edge cases or clarify interpretation.
Result / Impact:
- Hours of manual reading reduced to minutes.
- Source-backed answers improve confidence and accuracy.
- Decision-making becomes faster and more defensible.
- Knowledge becomes accessible to non-experts.
This demonstrates how RAG transforms dense documentation into actionable insight.
Business Decision Scenario
A medium-to-large automotive company is evaluating whether to invest in new manufacturing machinery.
- Financial statements
- Market trend reports
- Equipment performance data
- Historical ROI benchmarks
The AI retrieves relevant data points, synthesises them, and produces a recommendation—along with a confidence score (e.g. 90% likelihood of positive ROI). This allows leadership to act decisively, supported by evidence rather than intuition.
Pros and Cons
Pros
- Significantly improves AI accuracy and trustworthiness.
- Enables real-time insights from internal and external data.
- Reduces hallucinations common in standalone LLMs.
- Ideal for document-heavy and compliance-driven industries.
- Scales across departments and data sources.
Cons
- Requires upfront setup and data preparation.
- Dependent on data quality and retrieval configuration.
- More complex than using a generic chatbot alone.
- Infrastructure costs can grow with scale if unmanaged.
Integration & Compatibility
RAG systems integrate into modern business stacks via:
- Document ingestion (PDFs, Word files, spreadsheets).
- APIs connecting CRM, ERP, and data warehouses.
- Vector databases for semantic search.
- Front-end chat interfaces embedded into internal tools.
RAG works best when layered on top of existing systems rather than replacing them.
Support and Resources
Support for RAG implementations typically includes:
- Platform documentation and developer guides.
- AI provider tooling (e.g. embeddings, retrieval APIs).
- Consulting and implementation partners.
- Ongoing optimisation as datasets and business needs evolve.
Top 10 Common Inputs to a RAG System (Medium-Sized Businesses)
- Payroll data
- Employee handbook & policies
- CRM data
- Sales reports
- Inventory management data
- Financial statements
- Marketing performance data
- Customer feedback and reviews
- Product performance metrics
- Market trends and competitor analysis
These inputs allow businesses to centralise knowledge and make faster, data-driven decisions in real time.
If you’d like to learn more about how AI can help you grow, consider attending a Nimbull AI Training Day or reach out for AI Consulting services.
