New to RAG? You're not alone. Here are some answers to common questions about RAG.
When should I use RAG instead of a standalone LLM?
Use RAG when:
You need domain-specific accuracy (e.g., internal docs, research papers).
Your data changes frequently and isn’t in the LLM’s training set.
You want to reduce hallucinations by grounding responses in retrieved evidence.
Why are vector databases critical for RAG?
They handle billions of embeddings with millisecond search speeds. Without one, you’ll bottleneck on retrieval (e.g., brute-force search with FAISS works for small datasets but crashes at scale).
How do I optimize RAG pipeline costs?
Use smaller open-source embeddings (e.g., all-MiniLM-L6-v2) instead of OpenAI.
Cache frequent queries.
Benchmark LLMs: Claude Haiku vs. GPT-3.5 vs. Llama-2 for cost/accuracy tradeoffs.
Can RAG work with real-time/streaming data?
Yes! Use:
Incremental indexing (e.g., Milvus’ auto-flush).
Embedding models that update dynamically (e.g., BAAI/bge-small-en-v1.5).
How do I improve RAG answer accuracy?
Chunk smarter: Experiment with sizes (256 vs. 512 tokens) and overlap.
Add metadata filters (e.g., date, source).
Use hybrid search (vector + keyword).
Open-source vs. proprietary components?
Open-source (LlamaIndex, Milvus, Mistral): Full control, cheaper, but DIY.
Proprietary (OpenAI, Zilliz Cloud): Plug-and-play, but vendor lock-in.(Add a “Best of Both Worlds” tutorial link.)
How do I evaluate RAG performance?
Retrieval recall: Are the right docs fetched?
LLM answer quality: Use metrics like ROUGE or human eval.
Latency: Aim for <500ms end-to-end for chat apps.
My RAG pipeline is slow. How do I scale it?
Vector DB tuning: Sharding, indexing (HNSW vs. IVF).
LLM optimizations: Model distillation, quantization.
