Caching is the fastest way to cut LLM cost — and the fastest way to serve a stale or wrong answer confidently if you cache the wrong thing.
Safe to cache
- Deterministic, fact-stable content: a static FAQ answer, a document summary for a document that never changes.
- Expensive intermediate steps: embeddings for content that doesn't change, retrieval results for a stable query.
Risky to cache
Anything tied to live or personalised data — a user's account balance, today's schedule, current inventory. A cache hit here means confidently wrong, not just slightly stale.
Set a sensible time-to-live for anything that changes, and always include a way to force a cache bypass for debugging. See semantic caching for RAG for the retrieval-specific version of this same trade-off.
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.
