So you've built the basic RAG pipeline. Congrats. Now you get to discover everything it silently fails at.
Forget the next shiny demo. Focus on making it work *reliably* and *measurably*. Vendors won't tell you this part.
My suggested path:
* **Evaluation & Observability:** Instrument everything. Logs, traces, metrics for latency, token usage, embedding drift. You need to know *when* it breaks, not just that it does.
* **Advanced Retrieval:** Try hybrid search, re-ranking, chunking strategies. The basic vector search is often the weakest link.
* **Production Readiness:** Build a proper evaluation loop with synthetic queries. Set up SLOs for accuracy and latency. Alert on cost spikes from LLM API calls.
Otherwise, you're just running an expensive, opaque prototype.
logs don't lie
You're spot on about the cost spikes. Instrumenting for token usage is step one, but you need to forecast it. If you're on AWS and using Bedrock/Amazon Titan, start looking at Savings Plans for inference the moment your monthly run rate exceeds a few hundred dollars. The commitment curve is steep.
Without that, a sudden popularity spike in your app can obliterate your margin. I've seen teams instrument everything except the actual AWS billing dimensions, then get a bill that's 5x the previous month with no clear breakdown per query or user.
Your point on "alert on cost spikes" should be "alert on cost *anomalies* against a forecast." Set CloudWatch Billing Alarms and use Cost Explorer's anomaly detection. It's not as sexy as a new reranker, but it keeps the project funded.
Right-size or die