Can you describe your experience with error detection and automatic recovery in FastAPI services, and how you have used Langgraph or similar tools to design LLM‑powered workflows, including prompt engineering techniques?

In FastAPI, we use middleware to log request/response cycles and capture exceptions. For auto‑recovery, we implement retry logic with exponential backoff for idempotent endpoints and circuit‑breaker patterns for external calls. When building LLM‑powered workflows, we use Langgraph to orchestrate calls to the model, passing prompts that include error context. Prompt engineering involves crafting prompts that ask the model to validate its output against a schema, and if it fails, to generate a corrective prompt. This loop is embedded in the graph, so if a node fails, the graph automatically triggers a recovery node that re‑prompts the model with additional constraints. This approach keeps the FastAPI service responsive while ensuring the LLM workflow can self‑correct.