Module 6: Building Basic RAG Systems Slides

Slide Outline

1. Building Basic RAG Systems - The complete retrieve-augment-generate pipeline with source attribution and citations
2. Learning Objectives - 4 outcomes for this module
3. Why This Module Matters - This is the core skill. Every RAG application — customer support, legal AI, medical AI, code assistant — uses this pipel
4. The RAG Pipeline - Lesson section from the full module
5. Source Attribution - Lesson section from the full module
6. Edge Cases - Lesson section from the full module
7. Common Mistakes to Avoid - 4 mistakes covered
8. Hands-On Labs - 2 hands-on labs
9. Key Takeaways - 5 points to remember

Learning Objectives

• Build a complete RAG pipeline from scratch
• Implement context injection and prompt augmentation
• Add source attribution and citations
• Handle edge cases: no results, conflicting sources, long context

Why This Module Matters

This is the core skill. Every RAG application — customer support, legal AI, medical AI, code assistant — uses this pipeline. Master it here, then optimize with advanced retrieval, agents, and production patterns in later modules.

Common Mistakes

• Not setting a similarity threshold — returning irrelevant chunks degrades quality
• Including too many chunks — more context is not always better, it dilutes focus
• Not instructing the model to cite sources — users cannot verify answers
• Forgetting to handle the "no results" case — the model will hallucinate to fill the gap

Key Takeaways

• RAG pipeline: embed query → retrieve chunks → augment prompt → generate answer
• Always include "answer ONLY from context" in the system prompt to reduce hallucination
• Source attribution builds trust — cite document title, section, and relevance score
• Handle edge cases: no results, conflicting sources, context overflow
• This basic pipeline is the foundation — advanced techniques (Module 7+) improve quality

Hands-On Labs

1. Build a Complete RAG Chatbot

   Build an end-to-end RAG system with FastAPI.

   40 min - Intermediate

   • Ingest a document corpus into Qdrant
   • Build the retrieve-augment-generate pipeline
   • Expose as a FastAPI endpoint
   • Test with domain-specific questions

   View lab files on GitHub

2. Add Citations and Source Attribution

   Make your RAG system cite its sources.

   25 min - Intermediate

   • Include source metadata in the prompt
   • Parse citations from the LLM response
   • Return sources with relevance scores
   • Handle "no relevant information" gracefully

   View lab files on GitHub