Production Systems
20 minLesson 12 of 14
Retrieval-Augmented Generation (RAG)
Ground LLM responses in your own data to build reliable LLM applications
Learning goals
- •Understand the RAG architecture
- •Learn to implement basic RAG systems
- •Know common RAG pitfalls and solutions
Generic LLMs
- Knowledge cutoff (don't know recent events)
- Can't access private data
- May hallucinate facts
RAG solves this by: 1. Retrieving relevant documents from your data 2. Augmenting the prompt with this context 3. Generating a response grounded in real information
RAG Architecture
User Query
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┌─────────────────┐
│ Embed Query │
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┌─────────────────┐
│ Vector Search │ ← Your document embeddings
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┌─────────────────┐
│ Top K Results │
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┌─────────────────┐
│ Augment Prompt │
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┌─────────────────┐
│ Generate │
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ResponseImplementation Considerations
Chunking Strategy
- Too small: loses context
- Too large: noise and irrelevance
- Sweet spot: 200-500 tokens with overlap
Retrieval Quality
- Number of results (k): balance relevance vs context size
- Similarity threshold: filter low-relevance results
- Hybrid search: combine semantic + keyword matching
Prompt Design
- Clearly separate context from question
- Instruct model to say "I don't know" if context doesn't contain the answer
- Consider citing sources
Common mistakes
×Not saying 'I don't know'—model may hallucinate if context lacks the answer
×Poor chunking—too large or too small chunks hurt retrieval
×Ignoring metadata—dates, sources, and document types improve relevance
×No evaluation—track retrieval quality and answer accuracy separately
Key takeaways
+RAG grounds LLM responses in your actual data
+Quality depends on both retrieval and generation
+Chunking strategy significantly impacts results
+Always include instructions for handling missing information