AI Practitioner Path · modules
From "what is a token?" to "I can red-team production AI systems." Tokens, prompts, RAG, fine-tuning, AI security — security mindset baked in.
Module 1 · AI Foundations — Tokens, Context & Cost
How LLMs actually work — tokenisation, context windows, embeddings, and the cost economics every Indian practitioner needs to know.
Module 2 · Prompt Engineering for Practitioners
Beyond LinkedIn tips. Structured prompting, few-shot, JSON output, tool use, and how to ship reliable prompts that don't silently regress.
Module 3 · Building Production AI Apps with RAG
APIs, vector databases, chunking strategies, agents — the moment AI goes from toy to production. Includes Slack-bot RAG architecture.
Module 4 · Fine-tuning & Custom Models
When APIs aren't enough — train, evaluate, deploy custom models on your own infra. LoRA, vLLM, evals, and the cost trade-offs.
Module 5 · AI Security & Red Teaming
Attack and defend AI systems — the field almost no one teaches. OWASP LLM Top 10, prompt injection, jailbreaks, guardrails, RAG poisoning, model extraction.