Est. Reading Time: 20 Mins Prereq: Python Basics
Intelligent Systems Track

AI & Automation: Roadmap

Tagline: Intelligence Over Information.

Bridging the gap between raw data and agentic intelligence. Focus: LLMs, Retrieval Augmented Generation (RAG), and AI Infrastructure.

Foundation

Level 100: LLM Fundamentals

  • Model Architectures: Transformers, BERT, and GPT logic.
  • Prompt Engineering: Chain-of-Thought and Few-shotting.
  • API Integration: Building with OpenAI, Anthropic, and Llama.

Architect’s Verdict: AI is not a feature; it is a new layer of the architectural stack.

Intermediate

Level 200: RAG Systems

  • Vector Databases: Pinecone, Milvus, and Weaviate.
  • Data Ingestion: Chunking strategies and embeddings.
  • Retrieval Logic: Hybrid search and semantic reranking.
Workbench Utility Free Tool
RAG Cost Estimator →

Architect’s Verdict: An LLM without your data is just a smart stranger; RAG makes it a specialized expert.

Advanced

Level 300: Agentic Ops

  • Agent Frameworks: LangGraph, CrewAI, and AutoGen.
  • AI Orchestration: Tool-calling and stateful memory.
  • Deployment: GPU provisioning and inference scaling.
Workbench Utility Free Tool
GPU Load Balancer Config →

Architect’s Verdict: True AI maturity is reached when the agent can reliably orchestrate the infrastructure.