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Agentic AI vs RAG (Retrieval-Augmented Generation)

Both are taught at techcadd. Here is how they differ on level, tools, projects and where each one can take you.

Side by side

The differences at a glance

Feature	Agentic AI	RAG (Retrieval-Augmented Generation)
Level	Intermediate	Advanced
Duration	4 Months	2 Months
Who can join	Open to any stream; no computer science degree needed. Python basics are taught from scratch	Open to any stream; no machine learning background needed. Python basics are taught from scratch
Key tools	Python, Jupyter, VS Code, Git and GitHub, OpenAI, Anthropic Claude	Python, Jupyter, VS Code, Git and GitHub, OpenAI, Anthropic Claude
What you’ll learn	Module 1: Foundations of AI, LLMs and Agentic AI · Module 2: Python for AI Agents · Module 3: Prompt and Context Engineering · Module 4: Tool Calling and Function Execution	Module 1: Foundations of LLMs and RAG · Module 2: Python for AI Applications · Module 3: Document Processing and Chunking · Module 4: Embeddings and Vector Databases
Portfolio projects	—	—
Career roles	AI agent developer, AI automation engineer, LLM application developer	RAG developer, LLM application developer, AI engineer

Which to pick

Design and deploy AI agents that plan, use tools and complete multi-step tasks on their own.

Build AI assistants that answer accurately from your own documents, websites and databases.

Explain how RAG works and when to use it instead of fine-tuning
Process messy real-world documents and choose sensible chunking and embedding settings
Build and tune a retrieval pipeline with hybrid search and reranking

A counsellor can map either course to your degree, schedule and career goal — one call is usually enough.