CourseProposedChanges
Certification Covered
Google GenAI Leader
Stack
Program Includes:
Description
Enterprise Generative AI Mastery is a practical program designed to equip you with the skills necessary to design, build, and deploy scalable Generative AI applications. You will progress beyond basic prompting and explore advanced techniques in prompt engineering, Retrieval-Augmented Generation (RAG), LLM pipelines, and agent-based workflows to address real enterprise challenges.
You will create reliable, scalable GenAI systems, deliver a comprehensive RAG solution, and develop user-facing applications from inception to deployment. The program also encompasses evaluation, monitoring, and lifecycle management utilizing modern observability and MLOps practices, ensuring that your creations are measurable and maintainable in a production environment.
This course integrates theoretical knowledge with extensive practical labs and two essential projects to facilitate the creation of real-world, enterprise-grade GenAI solutions.
Course Outcomes
Upon completion, participants will be able to:
✔ Attempt and pass the Google Certified Generative AI Leader Certification Exam
✔ Build and evaluate RAG pipelines using FAISS, Chroma, Weaviate, and Pinecone.
✔ Utilize Gemini, GPT, and LLaMA models through APIs or local inference.
✔ Construct AI workflows with LangChain (tools, chains, memory, agents).
✔ Manage prompts, versions, and evaluations using Langfuse.
✔ Architect and develop AI Agent systems.
✔ Build and deploy Streamlit-based GenAI applications.
✔ Deliver two complete projects (RAG application + AI agent application).
Prerequisites
- Basic Python Programming (Essential Concepts Required)
- Core Python concepts including data types, functions & arguments, loops and conditionals, file handling (reading/writing JSON, text, CSV), error handling, and object-oriented programming (OOP).
- Python Packages & Modules
A solid understanding of commonly used Python packages such as pandas, json, requests, numpy, matplotlib, seaborn, os, logging, and others. - Understanding of APIs and JSON
A grasp of fundamental IT and software engineering concepts including APIs, HTTP, JSON, JSON parsing, Postman, CI/CD, cloud platforms, and browser interactions. - Basic Familiarity with Development Tools
Participants should have experience with VS Code (extensions, terminal, environment setup), Git & GitHub (clone, commit, pull, push — basic), and browser-based tools for using API dashboards like OpenAI/Gemini. - No Prior AI/ML Knowledge Required
This program covers the foundational concepts of AI and ML, requiring no prior experience in these fields. - Foundations of Google Cloud
An understanding of Google Cloud basics and common services related to storage, computing, databases, networking, AI, and analytics.
Course Content
1LLM Foundations for Production GenAI2 Sections
1.1 — How LLMs Work: Tokens, Context, Sampling, and Inference Choices
1.1 — How LLMs Work: Tokens, Context, Sampling, and Inference Choices
1.2 — LLM Behavior and Evaluation: Measuring Quality, Safety, and Consistency
1.2 — LLM Behavior and Evaluation: Measuring Quality, Safety, and Consistency
2Prompt Engineering Mastery2 Sections
2.1 — Prompt Patterns: From Simple Prompts to Structured Outputs
2.1 — Prompt Patterns: From Simple Prompts to Structured Outputs
2.2 — Enterprise Prompting: Debugging, Optimization, and Reliability
2.2 — Enterprise Prompting: Debugging, Optimization, and Reliability
3PromptOps: Advanced Prompting + Prompt Management2 Sections
3.1 — Advanced Prompting Patterns for Complex Workflows
3.1 — Advanced Prompting Patterns for Complex Workflows
3.2 — PromptOps: Enterprise-Grade Prompt Management and Governance
3.2 — PromptOps: Enterprise-Grade Prompt Management and Governance
4Automating Enterprise Workflows with LLM Pipelines2 Sections
4.1 — Designing LLM workflows: Templates Parsing and Chains
4.1 — Designing LLM workflows: Templates Parsing and Chains
4.2 — Advanced Pipelines: Tool Use, Memory and Interruption
4.2 — Advanced Pipelines: Tool Use, Memory and Interruption
5Embeddings, Semantic Search and Retrieval-Augmented Generation2 Sections
5.1 — Embeddings and Vector-Based Information Retrieval
5.1 — Embeddings and Vector-Based Information Retrieval
5.2 — RAG Pipeline Design and Optimization
5.2 — RAG Pipeline Design and Optimization
6Enterprise Agentic AI Workflows1 Sections
6.1 — AI Agent Architecture and Reasoning Models
6.1 — AI Agent Architecture and Reasoning Models
7Generative AI Application Engineering2 Sections
7.1 — LLM Pipeline Development and Orchestration
7.1 — LLM Pipeline Development and Orchestration
7.2 — User Interface Design for Generative AI Applications
7.2 — User Interface Design for Generative AI Applications
8Fine-Tuning and Customizing Large Language Models2 Sections
8.1 — Foundations of LLM Fine-Tuning and Adaptation
8.1 — Foundations of LLM Fine-Tuning and Adaptation
8.2 — Transfer Learning, Evaluation, and Production Deployment
8.2 — Transfer Learning, Evaluation, and Production Deployment
Overview of Capstone Projects
Retrieval-Augmented Generation (RAG) Application with an Interactive User Interface
Retrieval-Augmented Generation (RAG) Application with an Interactive User Interface
- Implement secure file upload and ingestion pipelines on Vertex AI (GCP) for unstructured and semi-structured data, including preprocessing and normalization for downstream search applications.
- Generate embeddings using the Gemini Embedding Model and store them in a scalable vector store (FAISS/Chroma), aligned with performance and growth requirements of the search index.
- Build a real-time RAG-powered Q&A system that retrieves relevant context from the search index and generates grounded responses using the Gemini Flash.
- Implement hierarchical summarization (short, medium, detailed) via prompt chaining to support varied user information depth and decision-making needs.
- Log user interactions, prompts, responses, and evaluation signals using observability and prompt-tracking tools on GCP for continuous monitoring and optimization.
- Develop a complete interactive UI (e.g., Streamlit or web app) featuring conversational chat, document search, and usage analytics, tightly integrated with the RAG backend.
Tech Stack
AI Agent System with an Interactive and User-Friendly Interface for Real-World Applications
AI Agent System with an Interactive and User-Friendly Interface for Real-World Applications
- Design and build autonomous agent-based systems using an LLM orchestration framework on Vertex AI (GCP), enabling reasoning, planning, and decision-making with Gemini Flash.
- Integrate external tools and actions such as search services, file readers, calculators, APIs, and custom data pipelines to support real-world task execution within enterprise workflows.
- Implement memory mechanisms (buffer, summary, and retrieval-augmented memory) to handle long-running, multi-turn interactions, optionally backed by a search index for knowledge-intensive tasks.
- Enable observability, tracing, and evaluation of agent behavior using enterprise-grade monitoring and feedback frameworks on GCP for continuous optimization.
- Develop a fully interactive user interface (e.g., Streamlit or web-based UI) for agent interaction, task execution, and result visualization, with flexible UI technology choices.
Tech Stack
Career Opportunities
Learning Roadmap
LLM Foundations for Production GenAI
2 Sections
Prompt Engineering Mastery
2 Sections
PromptOps: Advanced Prompting + Prompt Management
2 Sections
Automating Enterprise Workflows with LLM Pipelines
2 Sections
Embeddings, Semantic Search and Retrieval-Augmented Generation
2 Sections
Enterprise Agentic AI Workflows
1 Sections
Generative AI Application Engineering
2 Sections
Fine-Tuning and Customizing Large Language Models
2 Sections
Certified GenAI Engineer
Goal Achieved
Weekdays (Mon-Fri)
Starting from 16th March
Duration: 1.5 Hours per day
Weekend (Sat & Sun)
Starting from 21st March
Duration: 4 Hours per day
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Meet Our Leaders
Program Director
Set the direction of the program and ensure it meets industry and academic excellence standards.
Anand Singh
Blue Data Consulting
Program Manager
Manages schedules, coordination, assessments, and ensures smooth course delivery.
Janvi Bhandari
Blue Data Consulting
Industry Expert/Trainers
Harsh Dalal
Blue Data Consulting
AI Consultant
SME
Covers theory, hands-on labs, assessments, and daily assignments.
Suraj
Blue Data Consulting
Master SME
Specialized guidance for Capstone Projects and advanced implementation.
Apoorva
Blue Data Consulting
Mentor
Dedicated support to help all learners pass the certification exam.
Transparent Pricing
Invest in your future with our flexible payment options and exclusive scholarship benefits
Application Fee
5% of Total Fee (Non-refundable)
Program Fee
Complete program access
(After Application Fee + 20% Discount)
*18% GST & Certification cost included
Easy EMI Plans
Pay in flexible monthly installments
Starting from
Based on Program Fee (incl. GST)
Frequently Asked Questions
Why should you join this program?
This program is designed for professionals who want to move beyond basic prompting and build real, production-ready Generative AI systems. Unlike surface-level AI courses, this training focuses on enterprise use cases, hands-on implementation, and best practices used by real AI engineering teams. You will gain practical experience in building RAG systems, AI agents, and end-to-end GenAI applications that are scalable, reliable, and deployable in real business environments.
What will I be able to do by the end of this course (in practical terms)?
You will learn how to design, build, evaluate, and deploy enterprise-grade Generative AI solutions, including:
- Advanced prompt engineering and PromptOps
- Retrieval-Augmented Generation (RAG) using FAISS, Chroma, Pinecone, and Weaviate
- LLM pipelines and orchestration using LangChain
- AI agents with tool usage, memory, and reasoning
- Prompt versioning, monitoring, and evaluation using Langfuse
- Building interactive GenAI applications using Streamlit
- Model usage across Gemini, GPT, and LLaMA (API and local inference)
- Fine-tuning fundamentals and production deployment strategies
- By the end, you will have built two complete capstone projects ready for portfolio and enterprise demonstration.
Eligibility for this program?
Participants should have:
- Basic Python programming knowledge
- Understanding of APIs, JSON, and core software engineering concepts
- Familiarity with development tools like VS Code and Git (basic level)
- AI or Machine Learning is required.
Who can join this program?
- Software Developers and Engineers
- AI/ML Engineers and Data Scientists
- Data Analysts and Data Engineers
- DevOps and MLOps professionals
- Enterprise automation and innovation teams
- Professionals transitioning into GenAI engineering roles
How will this program benefit you?
- Build production-ready GenAI applications
- Gain hands-on experience with enterprise tools and workflows
- Strengthen your portfolio with real-world projects
- Improve career prospects in AI engineering and applied LLM roles
- Understand how to design reliable, scalable, and maintainable AI systems
- Stand out with practical skills that organizations actually need
- Globally recognized certification
How is the course delivered—live sessions, recordings, labs, and support?
The program follows a hands-on, instructor-led approach, combining:
- Concept explanation with real-world examples
- Live coding
- Guided labs and exercises
- Step-by-step project development
- Continuous doubt-clearing and mentorship
- Every concept is reinforced through implementation, not just slides.
How is this program format different from other courses?
Unlike generic AI courses, this program:
- Focuses on enterprise-grade engineering, not demos
- Emphasizes real tools like LangChain, Langfuse, FAISS, and Streamlit
- Includes two mandatory capstone projects
- Covers PromptOps, observability, and evaluation — often skipped elsewhere
- Teaches production reliability, monitoring, and deployment practices
- This makes it highly practical and job relevant.
How to enroll in this course?
- Registering through the course enrolment page on our website.
- Fill in your details, complete your registration by paying the registration fee.
- Receiving onboarding details and learning access via email.
What is the duration of the program?
The total duration of the program is 40 hours, covering live sessions, hands-on labs, and capstone project development.
How will the program be delivered?
The program is delivered in an instructor-led online format, with:
- Live interactive sessions
- Hands-on labs
- Real-time coding and demonstrations
- Project-based learning
How long will you have access to the learning material?
Participants will receive extended access to:
- Session recordings
- Learning resources
- Code samples and project templates
- This allows learners to revise concepts and practice even after program completion.
What if I miss a live class due to work / time zones (USA/Canada/Europe/Australia)?
If you miss a live session:
- You can access the session recording
- Supporting materials and code will be available
- You can clarify doubts in subsequent sessions or support channels
- This ensures you never fall behind.