Building an AI Dream Analysis Engine, Part 2: Designing a Production-Ready LLM Pipeline

Building a reliable AI dream analysis engine requires much more than sending text directly to GPT. This second installment upgrades the system by adding structured prompt engineering, embeddings, vector database search, and Retrieval-Augmented Generation (RAG), which feeds GPT trusted knowledge before generating responses. The result is a consistent, structured JSON output with confidence scores, emotions, and themes — reducing hallucinations and making the data easy for apps to display.