Both are taught at techcadd. Here is how they differ on level, tools, projects and where each one can take you.
| Feature | Artificial Intelligence | RAG (Retrieval-Augmented Generation) |
|---|---|---|
| Level | Intermediate | Advanced |
| Duration | 6–9 Months | 2 Months |
| Who can join | No prior coding experience; basic school-level maths (percentages, simple algebra) | Open to any stream; no machine learning background needed. Python basics are taught from scratch |
| Key tools | Python, Jupyter Notebook, Google Colab, NumPy, pandas, Matplotlib | Python, Jupyter, VS Code, Git and GitHub, OpenAI, Anthropic Claude |
| What you’ll learn | Module 1: Python for AI · Module 2: Data Handling and Maths Essentials · Module 3: Data Analysis and Visualisation · Module 4: Machine Learning | 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/ML trainee, Data analyst, Junior data scientist | RAG developer, LLM application developer, AI engineer |
Begin with Python and the essential maths, then move into machine learning, deep learning, natural language processing and generative AI, applying each concept through hands-on projects.
Build AI assistants that answer accurately from your own documents, websites and databases.
A counsellor can map either course to your degree, schedule and career goal — one call is usually enough.
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