LLM Systems and RAG: Building Useful AI Beyond Prompt Demos

This is Post 3 in the AI Series. The previous post covered MLOps and evaluation.

Why RAG Became Default

LLMs are strong general reasoners but weak on private, current, or domain-specific facts. Retrieval-Augmented Generation (RAG) solves this by injecting grounded context at inference time.

RAG Pipeline

1. Chunk and clean source documents
2. Create embeddings
3. Index in vector database
4. Retrieve top-k relevant chunks
5. Re-rank / filter
6. Generate answer with citations

Design Pitfalls

• Overly large chunks reduce precision.
• Missing metadata hurts filtering.
• No eval set means no measurable quality.
• Ignoring latency budgets kills UX.

Practical Guardrails

• Structured output schemas
• Tool calling with explicit permissions
• Citation enforcement
• Human escalation for high-stakes tasks

References

• Lewis et al., Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks: https://arxiv.org/abs/2005.11401
• OpenAI cookbook (RAG patterns): https://cookbook.openai.com/
• LangChain docs (retrieval architecture): https://python.langchain.com/docs/concepts/rag/

Best Books

• Jurafsky & Martin, Speech and Language Processing (latest draft).
• Chip Huyen, AI Engineering.
• Building LLM apps community playbooks by O’Reilly (practitioner references).