Skip to main content

​
Lesson 8: RAG in the Real World

​
From Concept to Impact

You understand how RAG works: retrieval, augmentation, generation. You know about embeddings, knowledge bases, and prompt design. But understanding the mechanics is different from seeing the transformation RAG enables. In this lesson, we’ll explore how RAG is being used to solve real problems across different domains. These aren’t hypothetical scenarios; they’re the kinds of applications being deployed today. By the end, you’ll have concrete ideas for how RAG could help in your own context.

​
Core Concepts

​
Customer Support: Answering Questions from Documentation

This is one of the most common RAG applications, and for good reason. Customer support involves answering questions from a knowledge base. That’s exactly what RAG does. The problem without RAG:
  • Support agents spend time searching for answers
  • Customers get inconsistent responses depending on who helps them
  • Self-service chatbots give generic, unhelpful answers
  • Documentation exists but customers can’t find what they need
With RAG: A customer asks: “How do I connect my device to WiFi?” The system retrieves the relevant section from the device manual, then generates a clear, step-by-step answer specific to that customer’s product. If the customer follows up with “It still won’t connect,” the system can retrieve troubleshooting steps. What makes it work:
  • A comprehensive knowledge base of product documentation, FAQs, and support articles
  • Good chunking that captures individual procedures and answers
  • Prompts that instruct the AI to acknowledge when it can’t help and offer to escalate
Results: Organizations using RAG for customer support often see faster response times, higher customer satisfaction, and reduced load on human agents (who can focus on complex issues that actually need human judgment).

​
Research Assistance: Synthesizing Information

Researchers deal with information overload. There are too many papers, reports, and sources to read comprehensively. RAG helps by finding and synthesizing relevant information. The problem without RAG:
  • Hours spent searching for relevant papers
  • Risk of missing important work that uses different terminology
  • Difficulty synthesizing findings across many sources
  • Information scattered across databases, journals, and repositories
With RAG: A researcher asks: “What methods have been used to measure employee engagement in remote work settings?” The system searches a database of academic papers, retrieves relevant sections from multiple studies, and synthesizes a summary of the methodologies used, with citations. What makes it work:
  • A knowledge base of academic papers and reports (often domain-specific)
  • Semantic search that finds conceptually related work even with different terminology
  • Generation that can synthesize across multiple sources while maintaining attribution
Results: Researchers can accelerate literature reviews, discover connections they might have missed, and stay current with new publications in their field.

​
Enterprise Knowledge: Making Institutional Knowledge Accessible

Every organization has institutional knowledge: how things work, why decisions were made, what’s been tried before. This knowledge lives in documents, wikis, emails, and people’s heads. RAG makes it searchable and accessible. The problem without RAG:
  • New employees take months to learn how things work
  • Knowledge leaves when employees leave
  • The same questions get asked repeatedly
  • Documentation exists but nobody can find it
  • Decisions get made without knowing what was tried before
With RAG: An employee asks: “What’s our process for vendor approval?” The system searches internal wikis, policy documents, and process guides, then generates a clear explanation with links to the relevant forms and contacts. What makes it work:
  • Indexing across multiple internal systems (wikis, document stores, intranets)
  • Appropriate access controls (people should only retrieve information they’re authorized to see)
  • Regular updates as policies and processes change
  • Prompts that encourage the AI to point to authoritative sources
Results: Faster onboarding, better decision-making, reduced repeated work, and preservation of institutional knowledge.

​
Personal Knowledge Management: Your AI-Powered Second Brain

RAG isn’t just for organizations. Individuals can use it to build personal knowledge systems. The problem without RAG:
  • You’ve read hundreds of articles, books, and papers but can’t remember where you saw something
  • Notes are scattered across apps and never consulted
  • Useful information falls into the void of “I know I read about this somewhere”
With RAG: You ask: “What were the key arguments in that book about habit formation I read last year?” Your personal RAG system searches your notes, highlights, and annotations, then summarizes the main points from that specific book. What makes it work:
  • A personal knowledge base of notes, highlights, and saved content
  • Regular capture of information you want to remember
  • A search interface that understands what you’re looking for
Results: Your reading and note-taking becomes cumulative. Nothing gets lost. Past learning actively supports current thinking.

​
Gloo AI Studio: RAG with Values Alignment

Platforms like Gloo AI Studio are making RAG accessible while emphasizing something often overlooked: values alignment and safety. What this means:
  • Content can be enriched and organized for effective retrieval
  • AI responses are grounded in provided materials rather than unrestricted generation
  • The system is designed to produce helpful results while avoiding harmful outputs
  • Publishers can control what content is available and how it’s used
Why it matters: RAG systems that draw from curated, appropriate knowledge bases are more likely to produce responses aligned with organizational values. This is especially important in sensitive domains: education, spiritual guidance, healthcare information, and other areas where the quality and appropriateness of information matters as much as its accuracy. Gloo’s approach combines the power of RAG with intentional design around what knowledge is retrieved and how it’s presented.

​
Specialized Domains

Beyond these common applications, RAG is transforming specialized fields: Legal research: Finding relevant case law, statutes, and legal opinions. The stakes are high, so accuracy and citation are critical. Medical information: Helping patients understand conditions and treatments by retrieving from peer-reviewed sources. Careful to distinguish information from advice. Financial services: Answering questions about products, regulations, and procedures from authoritative internal documentation. Education: Helping students learn by retrieving relevant explanations, examples, and resources tailored to their questions. Technical documentation: Helping developers find answers in API docs, code examples, and troubleshooting guides. Each domain has specific requirements around accuracy, citation, sensitivity, and compliance. RAG provides the foundation; domain expertise shapes the implementation.

​
Try It Yourself

​
Exercise 1: Identify Your RAG Opportunity

Think about your own work or life. Identify a situation where:
  1. You frequently need to find information in a collection of documents
  2. The information exists but is hard to find
  3. You often answer the same kinds of questions
  4. Knowledge feels scattered or inaccessible
This is your RAG opportunity. Describe it in a few sentences.

​
Exercise 2: Design the Knowledge Base

For the opportunity you identified:
  1. What documents or information would go in the knowledge base?
  2. Where does that information currently live?
  3. How often does it change?
  4. Who would need access?
Sketch out what the knowledge base would look like.

​
Exercise 3: Imagine the User Experience

Still working with your RAG opportunity:
  1. What questions would users actually ask?
  2. What would a great answer look like?
  3. What should happen when the answer isn’t in the knowledge base?
  4. How important is citation and source verification?
Write out three example interactions (question and ideal response).

​
Common Pitfalls

​
Pitfall 1: Solving the Wrong Problem

RAG is powerful, but it’s not the solution to everything. If the real problem is that your documentation is outdated or badly written, RAG will just make bad information more accessible. The fix: Before implementing RAG, ask: Is this actually a retrieval problem? Or is it a content quality problem?

​
Pitfall 2: Ignoring Access Control

In enterprise settings, not everyone should have access to all information. A RAG system that ignores permissions could expose sensitive data. The fix: Design access controls from the start. The retrieval system should respect the same permissions as the source documents.

​
Pitfall 3: Underestimating Maintenance

RAG systems need ongoing care. Content changes, user needs evolve, and retrieval quality can drift over time. The fix: Plan for maintenance from day one. Who updates the knowledge base? How do you monitor quality? What’s the feedback loop?

​
Pitfall 4: Overpromising to Users

Users might expect RAG to be perfect. When it gives wrong or incomplete answers, trust erodes quickly. The fix: Set appropriate expectations. Make it clear that the system is a helpful assistant, not an infallible oracle. Include easy paths to human help when needed.

​
Level Up

Here’s a planning challenge: Scenario: You’re proposing a RAG system for a nonprofit organization that provides guidance to volunteers working with at-risk youth. Consider:
  1. What would be in the knowledge base? (Training materials? Best practices? Legal guidelines? Crisis protocols?)
  2. What are the stakes if the system gives wrong information?
  3. How would you ensure responses are appropriate and safe?
  4. What role would human oversight play?
  5. How would you measure success?
Write a one-page proposal that addresses these questions. This exercise helps you think about RAG not just technically but holistically.

​
Key Takeaway

RAG is transforming how people access and use information across every domain: customer support, research, enterprise knowledge, personal productivity, and specialized fields. The common thread is taking information that exists but is hard to access and making it immediately useful through intelligent retrieval and grounded generation. Platforms like Gloo AI Studio make this accessible while emphasizing safety and values alignment. The opportunities are everywhere once you start looking.

​
What’s Next

You’ve seen RAG in action across many domains. But RAG isn’t magic, and it doesn’t work perfectly in all situations. In Lesson 9: When RAG Works (and When It Doesn’t), we’ll get honest about limitations. You’ll learn to recognize when RAG is the right tool, when it struggles, and how to evaluate whether your RAG system is actually working well. Understanding limitations is what separates effective practitioners from disappointed experimenters.