Practical guides to building with AI
Clear, hands-on explanations of how modern AI applications are designed and built, from the same material we teach in our workshops.
AI engineer roadmap 2026: from developer to AI engineer (India)
A practical AI engineer roadmap for Indian developers and students: Python, LLM APIs, agents, MCP, RAG, evaluation and production, with projects and timelines.
GuideWorkflow or AI agent? A practical decision framework
When to use a simple LLM workflow, a single AI agent or a multi-agent system: five questions, a worked support-ticket example and a checklist.
GuideHow to build a RAG application with citations (step by step)
Build a RAG app in Python that answers from your documents and cites its sources: chunking, embeddings, retrieval, prompts and validation.
ExplainerMCP (Model Context Protocol) explained for developers
What the Model Context Protocol is, how hosts, clients and servers fit together, a minimal Python MCP server, MCP vs function calling, and how to use it safely.
GuideHow to evaluate a RAG system: metrics, test sets and RAGAS
Measure RAG quality instead of guessing: build a test set, score retrieval and generation (faithfulness, correctness), use RAGAS, track every change.