How does RAG really work?

𝐌𝐨𝐬𝐭 𝐩𝐞𝐨𝐩𝐥𝐞 𝐭𝐡𝐢𝐧𝐤 𝐑𝐀𝐆 𝐰𝐨𝐫𝐤𝐬 𝐥𝐢𝐤𝐞 𝐭𝐡𝐢𝐬: 𝐐𝐮𝐞𝐬𝐭𝐢𝐨𝐧 → 𝐕𝐞𝐜𝐭𝐨𝐫 𝐃𝐁 → 𝐀𝐧𝐬𝐰𝐞𝐫. 𝐑𝐞𝐚𝐥𝐢𝐭𝐲?

That's probably just 10% of the story. After spending time building AI systems, one thing has become very clear: Great RAG systems are not built around vector databases. They're built around:

• Query rewriting
• Embeddings
• Reranking
• Context packing
• Evaluation
• Monitoring
• Guardrails

In other words: RAG is context engineering, not vector search. Ironically, many teams spend weeks debating models while overlooking the layers that determine whether the system succeeds or hallucinates. The difference between an impressive demo and a production-grade AI system usually isn't the model.

𝐈𝐭'𝐬 𝐭𝐡𝐞 𝐚𝐫𝐜𝐡𝐢𝐭𝐞𝐜𝐭𝐮𝐫𝐞.

I put together this visual breakdown to explain the hidden layers that most people never see.

 

 

 

 

 

 

 

 

Save this deck for the next time you're building a RAG system.

#ArtificialIntelligence #GenerativeAI #RAG #LLM #AIAgents #AIEngineering #ContextEngineering #MachineLearning #GenAI #AIArchitecture