Amit Upadhyay
Senior Software Engineer · AI Systems & Architecture
I write production-grade deep dives on AI architecture, model engineering, and the systems that make intelligent products actually work at scale.
AI Wisdom is where I document what the ecosystem glosses over — the hard engineering decisions between academic papers and a reliable production deployment.
With years of experience building full-stack platforms, I focus on areas most engineers hit walls: RAG pipeline design, token economics, multi-agent orchestration, and everything in between. No hype, no vibe-only benchmarks — just systems thinking grounded in code.
No hype. Just systems thinking.
- Benchmarking isolated AI nodes
- Documenting state-of-the-art token economics
- Providing code-first orchestration architectures
- Ensuring deterministic application state
From theory to deployment.
The ecosystem frequently jumps from academic papers straight to unreliable demos, skipping the crucial middle-layer: actual production engineering.
Every architecture, test, and system explored on this platform is structured to ensure reliability, observability, and scalability in live applications. No vibes-only metrics.
The goal is to provide a complete knowledge graph for designing autonomous systems that actually function under load.
Network Topologies
Inference Layer
System design for AI-powered generation — RAG pipelines, prompt orchestration, and context injection.
Access documentation →Integration Logic
Production API design, robust SDK integration, and deterministic fallbacks.
Access documentation →Model Runtime
Open-weights evaluation, continuous fine-tuning pipelines, and compute cost optimization.
Access documentation →Agentic Environment
Sandbox testing for multi-agent workflows, long-term memory execution, and tool use vectors.
Access documentation →Explore ideas with context.
AI Wisdom is built for practical learning: deep explanations, visual modes, experiments, and enough engineering context to make better architecture decisions.