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AI & ML track · Advanced

Generative AI and LLM Applications

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.

Duration
8 weeks
Guided hours
120 hrs
Projects
2
Mode
Live online
rag.py
# 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
The kind of code you write in Generative AI and LLM Applications
Outcomes

What you will be able to do

Tools and technologies covered:

PythonLLM APIsEmbeddingsVector DBRAGFastAPIEvals
  • Explain how LLMs generate text and where they fail
  • Write prompts that return structured, reliable output
  • Call LLM APIs with tools and streaming
  • Build retrieval augmented generation over your own documents
  • Evaluate answer quality, cost and latency
  • Ship an AI feature inside a web application
Syllabus

8 weeks, 6 modules

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.

  • Tokens, context windows and sampling
  • Prompt structure and examples
  • Structured output with JSON

  • Chat, streaming and tool use
  • Handling errors, retries and rate limits
  • Keeping API keys safe

  • What embeddings represent
  • Chunking documents
  • Vector databases and similarity search
  • Hybrid search and reranking

  • A RAG pipeline end to end
  • Citations and grounding
  • Reducing hallucinations
  • Conversation memory

  • Building an evaluation set
  • Guardrails and prompt injection
  • Tracking cost and latency

  • FastAPI backend and a chat UI
  • Deployment and monitoring
  • Final demo
Projects

What you will build

Every project lives on your GitHub and is reviewed like a real pull request.

Project 1
Syllabus Q&A bot

Ask questions over a university syllabus PDF and get answers with page citations.

Project 2
Support assistant

A RAG chatbot inside a web app, with an evaluation set and a cost dashboard.

Who it is for
  • Developers who want to add AI features to products
  • Learners who finished Machine Learning with Python
  • Full stack graduates moving towards AI engineering
What you need
  • Python and basic REST API experience
  • Machine Learning with Python, or equivalent
  • 12 or more hours a week
Roles you can apply for
AI EngineerLLM Application DeveloperFull Stack Developer (AI)Prompt Engineer

We provide placement support. We do not guarantee a job or a salary.

Fees

One fee, everything included

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.

Generative AI and LLM Applications

Full
EMI
₹24,999
  • 8 weeks, 120 guided hours
  • 2 reviewed projects
  • Recordings for one year
  • Resume review and mock interviews

Exclusive of 18% GST. EMI amounts are indicative; online checkout charges the full fee.

FAQ

Questions about this course

The course is not tied to one provider. Labs use more than one API so you learn the common patterns.

Lab credits are included for all course exercises.

You can follow it with Python and API experience, but the ML course makes the evaluation and embedding topics much easier.
Get Started

Book a free demo class

Sit in on a live 60-minute session of Generative AI and LLM Applications, meet the mentor and ask anything about the course.

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  • hello@example.com
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