Still treating ChatGPT like a search bar? Companies aren't hiring people who can type a question — they're hiring people who can build and evaluate a reliable prompt system. This is the course that gets you there.
Learn prompt design, evaluation, and RAG with LangChain and LlamaIndex the way production GenAI teams actually build it — ending in a final project you can show, not just a certificate.
By the time you finish this course, you will have built and evaluated a working document Q&A system — from prompt design through retrieval, evaluation, and observability. That RAG-plus-evaluation combination is exactly what separates a prompt engineer from someone who just types well. This course is how you get there in 8 weeks.
Syllabus
Phase 1: Prompt Design & LLM Fundamentals (Weeks 1–4)
Modules
Prompting Paradigm & Core Techniques
Structured Outputs & Function Calling
Prompt Evaluation (Manual & Automated)
Responsible Prompting & Intro to RAG
Vector Databases & Retrieval
LangChain & LlamaIndex Basics
Phase 2: RAG Systems, Evaluation & Final Project (Weeks 5–8)
Modules
Advanced RAG (Reranking, Hybrid Search, Agentic RAG)
RAG Evaluation & Observability
Framework Depth: LangChain & LlamaIndex for RAG
Final Project
Full session-by-session breakdown (all 24 sessions, 2-hour format) is in the downloadable curriculum PDF linked from the hero.
Outcome
By the end of this course, you will be able to:
- Write and structure zero-shot, few-shot, and chain-of-thought prompts that actually hold up
- Build evaluation harnesses that catch bad prompts before users do
- Ground LLM outputs with RAG: chunking, embeddings, and retrieval
- Build RAG applications with LangChain and LlamaIndex
- Apply advanced RAG techniques: reranking, hybrid search, agentic retrieval
- Recognize and mitigate hallucinations, bias, and prompt injection risks
- Track cost, latency, and quality with basic observability
- Deliver a final project: a working, evaluated RAG application
- Career roles you'll be ready for: Prompt Engineer, GenAI Application Developer, LLM Engineer (Junior)
Tools
Python
OpenAI
LangGraph
Vector Databases
Jupyter Notebook
Git & GitHub
LangSmith
Who Should Enrol
Complete beginner to GenAI?
Phase 1 starts with prompting fundamentals from zero. No prior ML or coding background required.
Already comfortable prompting ChatGPT or Claude day to day?
Every concept is taught with a working lab — you'll leave with an evaluated RAG application, not just a folder of saved prompts.
Developer curious about the GenAI stack?
Phase 1 moves fast through prompting fundamentals so you can focus your time on RAG, LangChain, and LlamaIndex.
Considering the full Prompt Engineering & LLM Ops track?
This is Course 1 of 2. Finish this, then decide separately whether LLM Ops & Agentic Systems is your next step.
Market Growth
Average Entry-Level AI Salary
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Senior AI Professional Salary
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Higher Salary with Advanced AI Skills
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Annual Growth of India's AI Market
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FAQs
Beginners, career-switchers, and working professionals who want to build and evaluate real GenAI applications — not just write clever prompts.
Basic Python helps but isn’t required on day one. Phase 1 starts with prompting fundamentals; Python becomes essential from Week 4 onward for RAG and frameworks.
This course covers prompting, evaluation, and RAG — the foundation every GenAI application needs. LLM Ops & Agentic Systems picks up from here and goes into agents, guardrails, and production deployment — this course is the prerequisite.
A final project: a working RAG application (e.g. a document Q&A system or support chatbot) with an evaluation harness and basic observability, presented as a live demo.
Each course stands on its own with its own certificate. Prompt Engineering Professional is a complete, job-ready skill set by itself — LLM Ops & Agentic Systems is there if you want to go further into agents and production.