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Post Graduate Certificate in Applied Generative Engineering and LLM Application

PG Certificate in Applied Generative Engineering & LLM Applications

Build and deploy production-grade LLM, RAG and agentic AI systems, end to end

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PG Certificate in Applied Generative AI Engineering & LLM Applications
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Prerequisites for PG Certificate in Applied Generative Engineering & LLM Applications

Prerequisites and Eligibility

Coding proficiency: Intermediate coding proficiency is required.

Prerequisites and Eligibility

Who can attend the PG Certificate in Applied Generative Engineering & LLM Applications course

Who is this program for?
  • ML Engineers & Generative AI Engineers
  • Data Engineers & Data Scientists
  • Technical Product Engineers & Solution Architects
  • Software Engineers & Backend Developers
  • Technical Product & AI Leaders
  • Implementation Consultant / Professionals
Who Should Attend
  • 500K+
    Professionals trained
  • 250+
    Workshops every month
  • 100+
    Countries and counting

PG Certificate in Applied Generative Engineering & LLM Applications

Course Highlights

Production-Focused Curriculum

Covering the complete journey from LLM foundations to building and deploying production-grade AI systems.

AWS-Integrated Learning

with a guided AWS path across Bedrock, OpenSearch, Lambda, API Gateway and SageMaker.

Evaluation & Reliability

with a structured approach to measuring LLM, RAG and AI agent performance.

24 Weeks of Live Learning

with 140+ hours of live online sessions led by industry practitioners.

Production-Grade Capstone

to build and deploy an end-to-end AI system as a portfolio-ready artefact.

Dual Completion Credentials

from iHUB DivyaSampark, IIT Roorkee and upGrad x AWS.

WHY CHOOSE UPGRAD KNOWLEDGEHUT FOR Applied Generative Engineering & LLM Applications?

upGrad KnowledgeHut Edge

Learn with a Global Community

Collaborate with a diverse cohort of learners from across industries, functions, and geographies. Exchange ideas, solve real-world business challenges, and build a professional network that extends beyond the classroom.

Learn from Industry Experts

Gain practical insights from experienced analytics professionals and industry practitioners who bring real-world business challenges, consulting perspectives, and applied AI use cases into every live session.

Weekend Learning Designed for Working Professionals

Balance learning with work through a structured blend of weekend live classes and self-paced learning. Progress through the curriculum without interrupting your professional commitments.

Career-Focused Learning Outcomes

Develop practical skills that employers value through hands-on projects, AI-assisted analytics workflows, dual capstone projects, and a portfolio demonstrating your ability to solve real business problems using data and AI.

PG Certificate in Applied Generative Engineering & LLM Applications Curriculum

Curriculum

1. Python, ML & AI Foundations

Module Overview 

Refresh the foundational Python, notebook and data skills required for the program, while getting hands-on with enterprise-grade AI development tools and environments.

Topics

  • Python fundamentals and refreshers 
  • Jupyter notebooks and development environments 
  • Data libraries and workflows 
  • AI development tools and environments 
  • Enterprise-grade tools for AI development 

Hands-on 

  • Python coding exercises 
  • Notebook-based data workflows 
  • Working with AI development environments 
  • Getting started with enterprise AI tools

2. Python, AI Engineering Environment & LLM Foundations

Module Overview

Establish the programming, systems and conceptual foundation required to build, test and integrate LLM-powered applications.

Topics

  • Python for AI applications 
  • Virtual environments and development workflows 
  • APIs and SDKs 
  • Git workflows 
  • Tokens and tokenisation 
  • Embeddings and context windows 
  • Transformer architecture and attention 
  • LLM model families and model-selection criteria 

Hands-on 

  • Build Python-based AI application components 
  • Work with APIs and SDKs 
  • Set up and manage development environments 
  • Work with Git-based development workflows 
  • Experiment with LLMs and model-selection criteria

3. Prompt Engineering, Structured Outputs & LLM Evaluation

Module Overview

Design reliable prompts and structured LLM interactions, and evaluate outputs for correctness, relevance, consistency and safety.

Topics

  • System and user prompts 
  • Few-shot prompting 
  • Prompt templates 
  • Chain-of-thought-aware design 
  • JSON and schema-constrained outputs 
  • Function and tool calling 
  • Prompt-injection risks 
  • Output quality metrics 
  • Test sets and evaluation rubrics 

Hands-on 

  • Design and test prompt strategies 
  • Create structured LLM outputs 
  • Implement function and tool calling 
  • Build test sets and evaluation rubrics 
  • Evaluate LLM outputs for quality and safety

4. Data Preparation, Embeddings & Vector Search

Module Overview

Build robust knowledge-preparation pipelines that make enterprise and domain data searchable and useful for LLM applications.

Topics

  • Document ingestion and parsing 
  • PDFs, web pages and structured data 
  • Data cleaning 
  • Metadata design 
  • Chunking strategies 
  • Embedding models 
  • Similarity metrics 
  • Vector indices 
  • Hybrid search and reranking 

Hands-on 

  • Ingest and prepare enterprise data 
  • Build document processing pipelines 
  • Generate and work with embeddings 
  • Create vector search workflows 
  • Implement hybrid search and reranking

5. Retrieval-Augmented Generation (RAG) Systems

Module Overview

Design, implement and evaluate end-to-end RAG systems that generate grounded, traceable and context-aware responses.

Topics

  • RAG architecture 
  • Query transformation 
  • Retrieval strategies 
  • Contextual retrieval 
  • Parent-child retrieval 
  • Citation generation 
  • Grounded generation 
  • Hallucination mitigation 
  • RAG evaluation 
  • Faithfulness, answer relevance and context precision 

Hands-on 

  • Build an end-to-end RAG pipeline 
  • Implement retrieval strategies 
  • Generate grounded responses with citations 
  • Apply hallucination mitigation techniques 
  • Evaluate RAG systems using key quality metrics 

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Course completion certificate

PG Certificate in Applied Generative Engineering & LLM Applications

Get a course completion certificate from iHUB DivyaSampark, IIT Roorkee

iHUB DivyaSampark IIT Roorkee Sample Certificate
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Course completion certificate

PG Certificate in Applied Generative AI Engineering & LLM Applications

Get a course completion certificate cobranded by upGrad x AWS

upGrad KnowledgeHut AWS sample certification
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PG Certificate in Applied Generative Engineering & LLM Applications Course FAQs

Frequently Asked Questions
Course FAQs

1. How is this program different from a typical Generative AI course?

The program goes beyond prompting and basic AI demos to cover the complete production journey—from RAG and agentic systems to API development, evaluation, LLMOps, observability, security and governance.

2. Will I work with real enterprise AI tools and technologies?

Yes. The program includes hands-on exposure to technologies such as AWS Bedrock, OpenSearch, Lambda, API Gateway, SageMaker, vector databases, Python, PyTorch and Sarvam APIs, integrated across the curriculum.

3. Will I learn how to evaluate whether an AI system actually works?

Yes. Evaluation is built into the learning journey, covering LLM evaluation, RAG evaluation, faithfulness, answer relevance, context precision and agent evaluation rather than focusing only on whether a system produces an output.

4. What will I build during the program?

You will work on LLM applications, RAG pipelines, tool-using agents and multi-agent workflows, culminating in a production-grade capstone where you design and build a deployable AI solution addressing a real business problem. 

Disclaimer

upGrad is a technology platform and is not a college or university. Any programme outcomes, including placements, salary growth, career progression, admissions, certifications, or other results, are not guaranteed and may vary based on individual circumstances and external factors. Certain programmes, certifications, examinations, memberships, and related services may be offered or provided by independent third-party institutions, universities, certificati