▸ Blog series
AI Architecture Decisions
The architectural forks every AI product hits — ML vs GenAI, RAG vs fine-tuning, context vs memory, vectors vs graphs, and where MCP fits.
- 01
Machine learning vs generative AI
Machine Learning predicts; Generative AI creates. The discriminative-vs-generative split decides your architecture, infra, and monthly cloud bill.
AI · 8 min read - 02
AI vs machine learning: an engineering deep dive
AI is the umbrella, machine learning is the engine. See how the distinction shapes your RAG pipelines, agentic systems, and Laravel or Shopify architecture.
AI · 8 min read - 03
LLM vs AI agent: from prompts to action
The architectural shift from LLMs to autonomous AI agents. How memory, tool-use, and planning turn a stateless model into a system that takes action.
AI · 7 min read - 04
RAG vs fine-tuning: choosing your AI architecture
RAG vs fine-tuning for production LLMs — knowledge vs behavior, latency, cost, privacy, and the hybrid approach. How to pick the right AI architecture.
AI · 7 min read - 05
Vector search vs graph search for RAG
Compare vector search and graph search for RAG. When to use embeddings via pgvector vs relationship-based knowledge graphs — and why GraphRAG often wins.
AI · 8 min read - 06
Context window vs memory: building AI that remembers
Stop context-stuffing your LLM prompts. The difference between the context window and persistent memory — and how RAG + pgvector scale AI agents affordably.
AI · 8 min read - 07
Prompt engineering vs context engineering
The shift from instruction design to data infrastructure — how context engineering uses RAG and MCP to build robust, accurate AI systems.
AI · 7 min read - 08
API vs MCP: connecting AI systems
The core differences between traditional APIs and the Model Context Protocol (MCP) — and when to use each to build scalable, agentic AI systems.
Architecture · 8 min read - 09
MCP vs A2A vs ACP: the agent protocol stack
MCP connects agents to tools, A2A connects agents to each other, and ACP has folded into A2A. How the two-layer agent protocol stack fits together in 2026.
Architecture · 9 min read - 10
MCP tool-use: building context-aware agents
Build context-aware agents with MCP. How tools, resources, and prompts let one server talk to any client — and kill brittle one-off integrations for good.
AI · 6 min read - 11
Training vs inference: scaling AI systems
How training and inference differ in compute, cost, and hardware, and how to architect each phase so your AI app stays fast and affordable at scale.
AI · 6 min read