Every LLM provider has occasional outages, and an app that depends entirely on one provider with no fallback plan goes fully dark the moment that provider does — often at the worst possible time.
Levels of graceful degradation
- Best: automatic fallback to a second provider or a smaller local model for critical functionality, so users barely notice.
- Good: a clear, honest error message and a queued retry, so the request isn't silently lost even if it can't complete immediately.
- Minimum: the rest of the app (anything not depending on the LLM) keeps working normally — an AI feature failing shouldn't take down unrelated functionality.
Building toward this
Isolate LLM calls behind a clear interface in your codebase so a fallback provider or a cached response can be swapped in during an outage without touching unrelated code.
See multi-provider LLM fallback.
GenAI engineer and AI product builder with 2+ years shipping production-grade AI systems: RAG pipelines, fine-tuned LLMs, hybrid retrieval and multi-modal apps across vision, speech and OCR, architected end to end from ingestion to deployment. Leads engineering for SCULT INDIA's 14-member team, founded the 500+ member TechVerse Enclave community and has mentored 200+ students. Three hackathon first prizes: Changethon 2025 (IIT Roorkee), Product Genesis at Vividhotsava 2025 (CSJMU Kanpur) and BYTEBATTLE (MeetKats).
Open to GenAI roles, hackathon judging, mentorship sessions and guest talks at colleges. Email pranjulrathour41@gmail.com.
