Introduction
Retrieval-Augmented Generation (RAG) combines the power of large language models with your own knowledge base, enabling AI agents to provide accurate, context-specific responses.
What is RAG?
RAG works by:
- Converting your knowledge base into embeddings (vector representations)
- Storing embeddings in a vector database
- Retrieving relevant context when answering questions
- Injecting context into the LLM prompt
Knowledge Base Construction
1. Content Collection
- FAQ documents
- Product documentation
- Company policies
- Case studies
- Pricing information
2. Chunking Strategy
Break documents into chunks (typically 300-500 characters) that preserve context while enabling precise retrieval.
3. Embedding Generation
Convert chunks into 384-dimensional vectors using Vertex AI embeddings.
Hybrid Search
TKC uses a three-pronged approach:
- Semantic Search: Vector similarity matching
- Keyword Search: Traditional text matching
- RRF Fusion: Reciprocal Rank Fusion combines results
RAG vs. Fine-Tuning
| RAG | Fine-Tuning |
|---|---|
| Easier to update knowledge | Requires retraining |
| Lower cost | Higher cost |
| Faster to implement | Slower to implement |
| Better for factual information | Better for style/tone |
Best Practices
- ✅ Use hybrid search for best results
- ✅ Chunk documents appropriately (300-500 chars)
- ✅ Include metadata (tags, categories) for filtering
- ✅ Regularly update knowledge base
- ✅ Monitor retrieval quality
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