rupreetg
I reviewed a codebase a few months back where the core AI feature was genuinely good. Clean model integration, fast responses, a UI the team had put real thought into. They were proud of it, and they had reason to be. Then I asked some questions. What happens when the
rupreetg
I've been in enough post-mortems to spot the pattern before it plays out. A team builds an agent. It handles the happy path well. The demo goes smoothly, the stakeholders nod, someone asks "when can we ship?" And that's where it gets quiet.
LLM Infrastructure
How prefix caching actually works, why most teams get the ordering wrong, and the exact restructuring that turns a cache-miss prompt into a cache-hit one. I once reviewed a system where every single call to the model was a cache miss. Not most calls. Every call. The team
LLM Infrastructure
Every team building with RAG right now has roughly the same setup. Documents get chunked, chunks get embedded, embeddings go into a vector database, and at query time you run a similarity search, grab the top-k chunks, and stuff them into the prompt. It works well enough to demo.
rupreetg
What it is, why it matters, and a working example you can run in five minutes. 1. What it actually is Neuro-symbolic AI is just two things working together, with a clear line between them. * The neural part. This is your LLM, or any model that reads messy human
AI Strategy
Everyone has AI now. The engineering team has code assistants. The support team has chatbots. The sales team has AI-powered CRM workflows. The marketing team has content generators. Every team lead, every VP, every board deck has a slide that says "AI-integrated" with a green checkmark
Production AI
Over 25 years in enterprise tech, and more intensely in the last couple of years building AI products and advising teams on agentic systems, I've watched the same pattern repeat itself so many times that I can predict it almost word for word. Someone gets pressure from the
Security
Agent security, governance, and why the industry is sleepwalking into production without guardrails. I watched an agent bypass its container and write to the host filesystem. Nothing catastrophic happened. It didn't run a destructive script. It didn't exfiltrate data. It just reached outside the boundary it
Agents
Prompt versioning, model routing, and why your agentic system's inference bill is lying to you. There's a pattern I keep seeing in teams building agentic systems. The architecture is solid. The agents work. The critic-falsifier loop catches real problems. Everything looks good in the demo.
LLM Infrastructure
Why debugging agentic systems is a fundamentally different problem — and what I've built to deal with it. In my last post, I talked about what makes a system genuinely agentic — the autonomous loop, the critic, the falsifier, the layered memory. If you haven't read it, start
Architecture
I've spent the last year architecting agentic systems — real ones, in production, tied to actual business workflows. And the more I build, the more I realize how loosely this industry throws around the word "agent." A team wraps an LLM API call in a Python function,