Intermediate Generative AI in 14 days
Prompt engineering, RAG, embeddings, vector databases, LangChain, and evaluating LLM outputs.
- Daily plan, 45-60 min a day
- 7 lessons + 21 exercises
- AI tutor included
- No prior coding experience required
- Practice tasks every day
- Build impressive portfolio project
What you will learn
A powered by mentors Intermediate plan with structured subtopics, quizzes, and practice tasks.
Advanced prompt engineering
Apply chain-of-thought, few-shot, and system prompt patterns for reliable outputs.
Retrieval-Augmented Generation (RAG)
Ground LLM answers in external documents using retrieval and context injection.
Embeddings and vector databases
Convert text to vectors and store/search them with Pinecone or Chroma.
Fine-tuning overview
Understand when and how to fine-tune models with LoRA and QLoRA.
LangChain: chains, agents, and tools
Build multi-step AI workflows with LangChain's chains and agent loops.
Evaluating LLM outputs
Score and test AI responses with LLM-as-judge and benchmark datasets.
Capstone: RAG-powered knowledge base
Build a document Q&A system using RAG, embeddings, and a vector database.
See the quiz + practice flow
Three answered questions and a filled code task so you know exactly what to expect.
Quiz preview
2/3 correct1. How long is the Generative AI plan?
Correct2. What level is this Generative AI plan?
Incorrect3. Which of these appears in the Generative AI outline?
CorrectCode practice preview
SubmittedFormat a lesson title
Build a helper that formats a lesson label with a padded index and title.
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