If your user queries are short "slang" phrases but your documents are formal legal texts, how do you ensure the embedding model can bridge that semantic gap?

How does the "Extraction, Transformation, and Loading" process differ when preparing data for a Vector Database versus a traditional SQL database?

A user asks "What were the sales in 2023?" How do you prompt the LLM to separate the semantic search ("sales") from the metadata filter ("year == 2023")?

What does a production-grade observability stack for AI agents look like? What metrics, logs, and traces are essential? How would you debug a scenario where an agent produces correct outputs 95% of the time but fails unpredictably?