Architecting Intelligence Labs
Deep dives, architecture blueprints, and hands-on systems for building real-world AI.
Featured Deep Dives
Comprehensive technical articles on AI systems, architecture, and production ML.
Architecting LLM Inference
KV cache, continuous batching, vLLM internals, speculative decoding, quantization, and serving architecture.
Architecting Reinforcement Learning for LLMs
RLHF, PPO, DPO, reward modeling, and alignment workflows.
ML System Design for Principal Engineers
Search, recommendations, forecasting, fraud systems, ML platforms, and production tradeoffs.
Demystifying AI Compute
GPUs, TPUs, accelerators, memory, networking, and the real economics of AI infrastructure.
Architecture Blueprints
Visual reference architectures for building real-world AI systems.
LLM Inference Blueprint
End-to-end architecture for high-throughput LLM serving with batching and caching.
Agentic AI Stack Blueprint
Multi-agent orchestration, tool calling, and state management architecture.
RAG Production Architecture
Retrieval-augmented generation with vector stores, chunking, and hybrid search.
ML Platform Blueprint
Feature stores, training pipelines, model registry, and inference infrastructure.
Distributed Training Blueprint
Data parallelism, model parallelism, and gradient synchronization at scale.
AI Evaluation Pipeline
Automated evaluation, regression testing, and quality gates for ML systems.
Watch the Visual Deep Dives
Whiteboard-style explanations and architecture walkthroughs for complex AI systems.
How LLM Inference Actually Works
Deep dive into the mechanics of token generation, attention, and serving.
KV Cache Explained Visually
Visual walkthrough of attention caching and memory optimization.
Agentic AI Stack: What Breaks and Where
Failure modes and reliability patterns for multi-agent systems.
OptiFlow Architecture Walkthrough
Technical deep dive into the OptiFlow optimization system.
Labs in Progress
Experimental tools, demos, and practical systems from Architecting Intelligence Labs.
OptiFlow
Generative AI-powered optimizer for distributed and parallel workloads across Spark, Ray, Kubernetes, Airflow, Dataproc, and cloud platforms.
YODA Evaluation Agent
AI-powered evaluation agent for model readiness, regression detection, and promote/canary/reject decisions.
RAG Evaluation Toolkit
Evaluation framework for retrieval quality, answer quality, hallucination, and production readiness.
LLM Inference Playground
Interactive demos for batching, KV cache, quantization, and serving tradeoffs.
Agentic AI Framework
Reference architecture and starter kit for multi-agent AI systems.
ML Platform Templates
Architecture templates for feature stores, training, inference, monitoring, and governance.
Books & Practical Guides
Download architecture playbooks, eBooks, templates, and checklists for building real-world AI systems.
LLM Inference Architecture Guide
Complete guide to building high-performance LLM serving infrastructure.
ML System Design Interview Guide
Preparation guide for senior ML system design interviews.
Agentic AI Architecture Patterns
Reference patterns for building reliable multi-agent systems.
RAG Production Readiness Checklist
Checklist for production-ready retrieval augmented generation.
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Built by an AI systems practitioner
Architecting Intelligence Labs is created by Pawan K Jha, an ML and AI systems leader with experience building large-scale ML platforms, forecasting systems, search and ranking platforms, GenAI systems, and AI architecture across enterprise environments. The mission is to make complex AI systems understandable, practical, and production-ready.