ARTICLE AD BOX
I’m building an LLM-based platform that currently supports capabilities such as RAG pipelines, model performance tracking, and cost monitoring/optimization.
My goal is to evolve this into a robust, production-grade system, and I’m trying to better understand the real-world challenges practitioners face when working with LLMs at scale. Rather than collecting generic feature requests, I’m looking for insights grounded in practical experience
Specifically, I’m looking for input on:
1. Missing but critical features
What are some must-have capabilities that are often overlooked when designing LLM platforms, but become essential in real-world usage?
2. Failure modes and edge cases
What kinds of failure scenarios should be explicitly handled?
(e.g., retrieval failures, hallucinations, latency spikes, tool/agent breakdowns, etc.)
3. Usability challenges
What are the most common pain points users face when interacting with LLM platforms (both developers and non-technical users)?
4. Collecting actionable feedback
What are effective ways to structure user feedback questions so that responses are specific, actionable, and implementation-friendly, rather than vague suggestions?
I’m particularly interested in lessons learned from teams that have deployed LLM pipelines in production—especially around scaling, reliability, and user experience.
Any concrete examples, checklists, or hard-earned insights would be highly valuable
