Lesson 10: Getting Started with RAG
From Understanding to Action
You’ve come a long way. You understand why RAG exists, how it works, and when to use it. You know about embeddings, knowledge bases, retrieval strategies, and prompt design. You can evaluate RAG quality and recognize its limitations. Now what? This final lesson is about turning knowledge into action. We’ll explore the different paths for implementing RAG, from no-code platforms to custom builds, and help you identify the right starting point for your situation.Core Concepts
The RAG Implementation Spectrum
RAG implementations exist on a spectrum from simple to complex: No-code platforms: Upload documents, get a working RAG system. No programming required. Fastest time to value, least customization. Low-code/API-based solutions: Use pre-built APIs and services to assemble RAG functionality. Some technical skill required, more flexibility than no-code. Framework-based development: Use open-source frameworks like LangChain or LlamaIndex to build custom RAG pipelines. Significant flexibility, requires development skills. Fully custom solutions: Build everything from scratch: embedding pipelines, vector databases, retrieval logic, prompt engineering. Maximum control, maximum effort. There’s no universally “right” choice. The best path depends on your goals, resources, and constraints.Starting with Platforms: Gloo AI Studio
For many users, especially those new to RAG or working in organizations that value safety, platforms like Gloo AI Studio offer an accessible entry point. What platforms typically provide:- Content upload and processing: Add documents and have them automatically chunked and embedded
- Built-in retrieval: Semantic search without building the infrastructure yourself
- Response generation: AI that uses retrieved content to answer questions
- User interface: Ready-to-use chat or search interfaces
- Safety guardrails: Responses designed to be helpful, appropriate, and values-aligned
- Values-aligned AI that focuses on human flourishing
- Safety considerations built into the platform
- Content enrichment through AI Studio
- APIs for developers who want to integrate RAG into their own applications
- You want to validate the RAG concept quickly
- You don’t have dedicated development resources
- Safety and values alignment are priorities
- Your use case fits the platform’s designed patterns
Building with APIs and Frameworks
If you need more customization, you can build RAG functionality using APIs and frameworks: Components you might use:- Embedding APIs: Services that convert text to embeddings
- Vector databases: Specialized databases for storing and searching embeddings (Pinecone, Weaviate, Chroma, etc.)
- LLM APIs: Language models for generation (available through various providers, including Gloo)
- Orchestration frameworks: Tools like LangChain or LlamaIndex that help connect these components
- Write code to process documents into chunks
- Use an embedding API to create vectors
- Store vectors in a vector database
- Write retrieval logic to search for relevant chunks
- Construct augmented prompts with retrieved content
- Call an LLM API to generate responses
- Present results in your application
- You need custom behavior that platforms don’t support
- You’re integrating RAG into an existing application
- You have development resources and time
- You want maximum control over every component
Preparing Your First Knowledge Base
Regardless of implementation approach, you’ll need content. Here’s a practical process: 1. Inventory your content: What documents, articles, or data do you want to make searchable? Where does it live? What format is it in? 2. Assess quality: Is the content accurate and up-to-date? Is it well-written? Are there gaps that need filling? 3. Prioritize: Start with the most valuable, highest-quality content. You can expand later. 4. Prepare for processing: Extract text from PDFs, clean up formatting issues, remove irrelevant headers/footers. 5. Decide on structure: How will you organize content? What metadata will you track? 6. Start small: Don’t try to index everything on day one. Start with a focused subset and validate the approach.The Iterative Approach: Start Simple, Measure, Improve
Here’s wisdom from experienced RAG practitioners: don’t try to build the perfect system from day one. Start simple:- A small, high-quality knowledge base
- Basic retrieval settings (default top-K, standard embedding model)
- Straightforward prompt templates
- Are users getting helpful answers?
- What questions fail? Why?
- What feedback are you getting?
- Expand the knowledge base based on what’s missing
- Tune retrieval based on what works and what doesn’t
- Refine prompts based on generation quality
Resources for Continued Learning
Your RAG journey doesn’t end with this curriculum. Here are areas for continued exploration: Deeper technical understanding:- How embedding models are trained and compared
- Vector database internals and optimization
- Advanced retrieval strategies (hybrid search, re-ranking, query expansion)
- Systematic evaluation frameworks for RAG
- User research methods for understanding needs
- A/B testing and continuous improvement
- Multi-modal RAG (images, audio, video)
- Agentic RAG (systems that take actions, not just answer questions)
- RAG for conversation (maintaining context across multiple turns)
- RAG for legal, medical, financial, or other specialized domains
- Compliance and security considerations
- Enterprise deployment patterns
Try It Yourself
Exercise 1: Assess Your Situation
Answer these questions to clarify your starting point:- Goal: What problem are you trying to solve with RAG?
- Content: What knowledge would you want the system to use?
- Users: Who would use this system? What questions would they ask?
- Resources: Do you have development capabilities, or do you need a no-code solution?
- Constraints: Are there security, compliance, or values requirements?
Exercise 2: Plan Your Pilot
Design a small pilot project to test RAG in your context:- Scope: What’s the smallest valuable version you could build?
- Content: What subset of content would you start with?
- Success criteria: How will you know if the pilot worked?
- Timeline: What’s a realistic timeframe for initial testing?
- Next steps: If the pilot succeeds, what would you do next?
Exercise 3: Explore Available Tools
Do some research on RAG implementation options:- Visit Gloo’s documentation to understand their platform capabilities
- Look at one vector database (Pinecone, Weaviate, or Chroma) to understand what they provide
- Explore LangChain or LlamaIndex documentation to see what framework-based development looks like
Common Pitfalls
Pitfall 1: Analysis Paralysis
With so many options for implementing RAG, it’s easy to get stuck comparing approaches instead of building anything. The fix: Pick a starting point and try it. You’ll learn more from building something imperfect than from endlessly researching the “best” approach.Pitfall 2: Starting Too Big
Trying to index all your content, serve all use cases, and satisfy all requirements on day one is a recipe for failure. The fix: Start with a focused pilot. Prove the concept, then expand.Pitfall 3: Underestimating Content Work
Many RAG projects stall because of content issues: documents aren’t available, need cleaning, have permissions problems, or turn out to be lower quality than expected. The fix: Assess your content situation realistically before committing to timelines. Content preparation often takes longer than the technical implementation.Pitfall 4: Forgetting the User
It’s easy to get caught up in technical details and forget that the point is to help users accomplish something. The fix: Start with user needs. What questions do they actually ask? What would make their lives better? Let that guide your implementation choices.Level Up
Here’s your final challenge: Create your RAG roadmap. Part 1: The Opportunity In 2-3 sentences, describe a specific RAG opportunity in your work or life. What information would be searchable? Who would benefit? Part 2: The Approach Based on your resources and constraints, which implementation path would you take? Why? Part 3: The Pilot Describe a minimal pilot: what content, what questions it would answer, how you’d measure success. Part 4: The Next Step What’s the single next action you would take to move forward? This roadmap is your transition from learning about RAG to doing something with RAG.Key Takeaway
RAG can be implemented across a spectrum from no-code platforms like Gloo AI Studio to fully custom solutions. The right choice depends on your goals, resources, and constraints. The best approach is to start simple, measure results, and improve iteratively. Don’t get stuck in analysis paralysis; pick a path and begin. Your first implementation won’t be perfect, but you’ll learn more from building than from planning. The knowledge you’ve gained in this curriculum gives you the foundation; now it’s time to build.Conclusion: You’re Ready
Congratulations. You’ve completed the RAG curriculum. You started by understanding the problem RAG solves: AI that’s limited by its training data, that hallucinates when it doesn’t know, that can’t access your specific information. You learned how RAG works: the three-step dance of retrieve, augment, generate. You explored embeddings that capture meaning, knowledge bases that organize information, retrieval that finds what matters, augmentation that structures prompts, and generation that produces grounded responses. You saw RAG in action across real-world domains and learned to recognize when it’s the right tool and when it isn’t. Now you’re ready to put this knowledge to work. Whether you use a platform like Gloo AI Studio with its focus on safety and values alignment, or build something custom, you have the conceptual foundation to make good decisions and create effective solutions. RAG is transforming how we access and use information. You now understand how to be part of that transformation. Go build something useful.Thank you for completing the RAG 103 curriculum. We hope this knowledge serves you well as you explore ways to make AI more helpful, more accurate, and more grounded in the information that matters to you.