Building Production LLM Applications in India: Complete Developer Guide
Learn how to build scalable Large Language Model applications in India covering architecture, RAG, cost control, monitoring, and deployment.
Neeraj Mehta
Senior ML Engineer
In brief
Learn how to build scalable Large Language Model applications in India covering architecture, RAG, cost control, monitoring, and deployment.
The full picture
Building an LLM demo is easy. Building a production-grade LLM system is a different engineering problem. In production, the real challenges are consistency, latency, safety, and cost control across thousands of requests.
For most teams, model selection should be use-case driven. Not every flow needs a premium model. A good architecture routes requests by complexity, uses caching where possible, and keeps prompts short and structured. This alone can save a large part of monthly spend.
RAG becomes important when answers must come from your own documents, policies, or product knowledge. The quality of chunking and retrieval usually matters more than model size. If retrieval is weak, answers will still feel generic.
Practical takeaway
add observability from day one. Track token usage, response time, failure modes, and user feedback. Teams that instrument early improve faster and avoid expensive guesswork.
