Prompting, embeddings, retrieval augmented generation and shipping an AI feature inside a web app.
A short, practical course on building with large language models. You learn how to get reliable output, ground answers in your own data, and measure quality and cost.
# Answer a question using your own documents
def answer(question: str) -> str:
query_vec = embed(question)
chunks = store.search(query_vec, top_k=4)
context = "\n\n".join(c.text for c in chunks)
prompt = (
"Answer using only the context below. "
"If the answer is not there, say so.\n\n"
f"Context:\n{context}\n\nQuestion: {question}"
)
reply = llm.chat(prompt, temperature=0)
return reply.text
Tools and technologies covered:
Each module ends with a graded assignment and a code review from your mentor. Classes run on weekday evenings and weekends, and every session is recorded.
Every project lives on your GitHub and is reviewed like a real pull request.
Ask questions over a university syllabus PDF and get answers with page citations.
A RAG chatbot inside a web app, with an evaluation set and a cost dashboard.
We provide placement support. We do not guarantee a job or a salary.
Live classes, recordings for one year, mentor code reviews, projects, and interview preparation. Pay in full online, or ask about monthly instalments at no added interest.
Placeholder fee. Change it in the admin area.Exclusive of 18% GST. EMI amounts are indicative; online checkout charges the full fee.
Sit in on a live 60-minute session of Generative AI and LLM Applications, meet the mentor and ask anything about the course.
A free, self-paced start in Python for anyone heading into the AI & ML track.
View course detailsFrom NumPy and pandas to trained, evaluated models on real Indian datasets.
View course details