Generative AI, RAG & AI Agents Online Training
Complete Applied Generative AI Online Training — LLMs, Prompt Engineering, Embeddings, Vector DBs, Advanced RAG, LangChain/LangGraph Agents, MCP, LLMOps & Capstone.
Technologies You Will Master
Everything Included: Complete Syllabus, Live Classes & Capstone
This focused 6-week (60 hours) live online training covers 100% of the Generative AI, RAG & AI Agents Online Training syllabus from core fundamentals to production architecture — culminating in a mentor-reviewed Hands-On Capstone Project you can showcase directly on GitHub and in technical interviews.
Course Overview & Architectural Focus
### 💻 Complete Live Online Training + Hands-On Capstone (₹4,999 All-Inclusive)
Generative AI, RAG & AI Agents Online Training is a focused, practical **6-Week (60-Hour) Live Online Training Course** engineered to take you through the **complete end-to-end syllabus** without bloated theory or overpriced ₹50,000+ career bundles. Every concept is coded live with industry mentors and reinforced through hands-on labs.
### 🚀 What Is Included in Your ₹4,999 Online Training
- **Complete Structured Syllabus (10 Modules)**: Step-by-step live online classes covering fundamentals, internal mechanics, performance best practices, and production patterns.
- **Hands-On Capstone Project**: Build, test, document, and deploy a real-world GitHub portfolio project reviewed by your trainer.
- **Topic-Wise Mock Tests & AI Viva**: Reinforce every lesson with timed MCQ diagnostics and interactive AI technical viva practice.
- **Direct Trainer Doubt Clearing & Callback Guidance**: Get personalized batch timing support (morning/evening/weekend) and 1-on-1 doubt resolution.
### 🎯 Who Should Join
Python/JS developers, backend engineers, product engineers, and students building production RAG systems, AI Copilots, and Autonomous Agents.
Technologies & Toolchain Mastered
Production-level tools and frameworks practiced during live online coding sessions and capstone development.
Structured Learning Path (4 Phases)
Week-by-week progression taking you from core foundations to your completed Capstone Project.
LLMs, Frontier APIs & Structured Prompt Engineering
Master tokenization, context windows, temperature/sampling, structured schemas, and few-shot/chain-of-thought prompting.
Embeddings, Vector Databases & Production RAG
Ingest PDFs, docs, and codebases into vector stores with hybrid retrieval, cross-encoder reranking, and citation grounding.
Autonomous AI Agents, LangGraph, Tooling & MCP
Build multi-step reasoning agents with deterministic tool execution, human-in-the-loop checkpoints, and Model Context Protocol.
LLMOps, Security Guardrails & GenAI Capstone
Evaluate faithfulness, block prompt injection, cut token latency/cost, and ship the Enterprise AI Copilot Capstone.
Complete Course Syllabus (10 Modules)
Every module includes live coding labs, 20+ topic mock tests per lesson, and AI technical viva practice.
LLM pretraining, tokenization, context windows, reasoning, capabilities, failure modes, hallucinations, and open-weight models. API keys, authentication, inference parameters, streaming, retries, rate limiting, cost tracking, and reusable LLM service design.
Hands-on concept explanation, live coding examples, and practice exercises for LLM Fundamentals & Pretraining: Self-Supervised Objectives, Next-Token Prediction, Context Windows & Compute Scaling.
Hands-on concept explanation, live coding examples, and practice exercises for LLM Reasoning Capabilities: Text Generation, Code Generation, Extraction, Summarization, Q&A & In-Context Learning.
Hands-on concept explanation, live coding examples, and practice exercises for LLM Limitations & Failure Modes: Hallucinations, Context Loss, Knowledge Cutoffs, Bias, Latency & Cost Economics.
Hands-on concept explanation, live coding examples, and practice exercises for Model Landscape: Frontier Models (OpenAI, Anthropic, Gemini) vs Open-Weight Models (Llama 3, Mistral, Gemma).
Hands-on concept explanation, live coding examples, and practice exercises for LLM API Architecture: Client Initialization, API Keys, Request/Response Payloads & Model Endpoints.
Hands-on concept explanation, live coding examples, and practice exercises for Inference Parameters: Temperature, Top-p (Nucleus Sampling), Max Tokens, Stop Sequences & Presence Penalties.
Hands-on concept explanation, live coding examples, and practice exercises for Production Client Engineering: Retries with Exponential Backoff, Timeouts, Rate Limiters, Token Counters & Fallbacks.
Hands-on concept explanation, live coding examples, and practice exercises for Reusable LLMService Class Design: Implementing generate(), stream(), embed(), classify() & extract() Methods.
Instructions, context, zero/few-shot prompting, Chain-of-Thought, structured JSON outputs, Pydantic validation, and security.
Hands-on concept explanation, live coding examples, and practice exercises for Prompt Engineering Foundations: System Prompts, User Inputs, Context Framing, Delimiters & Formatting Constraints.
Hands-on concept explanation, live coding examples, and practice exercises for Advanced Prompting Techniques: Zero-Shot, Few-Shot In-Context Learning, Role Prompting & Chain-of-Thought (CoT).
Hands-on concept explanation, live coding examples, and practice exercises for Structured JSON Outputs: Schema Enforcement, Pydantic Schema Validation & Tool Calling Integration.
Hands-on concept explanation, live coding examples, and practice exercises for Prompt Security & Defense: Prompt Injection, Indirect Injection, Jailbreak Prevention & Instruction Hierarchy.
Dense vector embeddings, semantic similarity, distance metrics, chunking strategies, and end-to-end vector pipelines. Vector indexing, pgvector, Chroma, Qdrant, FAISS, vector CRUD, metadata filtering, hybrid search, and HNSW graph indexing.
Hands-on concept explanation, live coding examples, and practice exercises for Vector Embeddings Fundamentals: High-Dimensional Semantic Spaces, Dense Vectors & Semantic Proximity.
Hands-on concept explanation, live coding examples, and practice exercises for Vector Similarity Metrics: Cosine Similarity, Dot Product, Euclidean Distance & Angular Distance.
Hands-on concept explanation, live coding examples, and practice exercises for Embedding Applications: Semantic Search, Document Retrieval, Recommendation, Clustering & Deduplication.
Hands-on concept explanation, live coding examples, and practice exercises for Practical Implementation: End-to-End Document Chunking and Embedding Generation Pipeline.
Hands-on concept explanation, live coding examples, and practice exercises for Vector Database Architecture: High-Dimensional Indices, Approximate Nearest Neighbor (ANN) & Vector Stores.
Hands-on concept explanation, live coding examples, and practice exercises for Vector Technologies: pgvector for PostgreSQL, Chroma, Qdrant & FAISS Architecture Comparisons.
Hands-on concept explanation, live coding examples, and practice exercises for Vector CRUD Operations: Inserting, Upserting, Querying Top-K, Metadata Filtering & Payload Management.
Hands-on concept explanation, live coding examples, and practice exercises for Indexing Algorithms & Hybrid Search: HNSW Graph Indexing, IVFFlat, Keyword BM25 + Vector Hybrid Retrieval.
RAG lifecycle, document parsing, fixed vs semantic chunking, vector retrieval, context injection, and citation attribution. Query rewriting, multi-query expansion, HyDE, cross-encoder reranking, context compression, and Ragas evaluation.
Hands-on concept explanation, live coding examples, and practice exercises for RAG Architecture & Lifecycle: Why RAG, External Knowledge Grounding, Retrieval + Augmentation + Generation.
Hands-on concept explanation, live coding examples, and practice exercises for Document Ingestion & Chunking: PDF/DOCX Parsing, Fixed-Size vs Sentence-Window vs Semantic Chunking & Overlaps.
Hands-on concept explanation, live coding examples, and practice exercises for Retrieval & Context Construction: Vector Similarity Search, Metadata Filtering, Top-K Selection & Prompt Assembly.
Hands-on concept explanation, live coding examples, and practice exercises for Grounded Generation & Inline Citations: Context Injection, Hallucination Reduction, Citation Tracking & Page References.
Hands-on concept explanation, live coding examples, and practice exercises for Advanced Query Processing: Query Rewriting, Multi-Query Expansion, Step-Back Prompting & HyDE (Hypothetical Embeddings).
Hands-on concept explanation, live coding examples, and practice exercises for Reranking & Hybrid Search: Cross-Encoder Models, Cohere Rerank API, Combining Dense Vectors & Sparse BM25.
Hands-on concept explanation, live coding examples, and practice exercises for Context Compression & Position Optimization: Mitigating 'Lost in the Middle', Summarization & Dynamic Context Trimming.
Hands-on concept explanation, live coding examples, and practice exercises for RAG Evaluation Metrics: Retrieval Precision, Retrieval Recall, Context Relevance, Faithfulness & Answer Relevancy.
PDF ingestion, OCR for scanned documents, table detection, form extraction, and multi-page document processing pipelines.
Hands-on concept explanation, live coding examples, and practice exercises for Complex Document Ingestion: Handling Complex PDFs, Scanned Documents, OCR with Tesseract & Textract.
Hands-on concept explanation, live coding examples, and practice exercises for Table & Structured Data Extraction: Detecting Table Boundaries, Extracting Tabular Matrices & Form Key-Values.
Hands-on concept explanation, live coding examples, and practice exercises for End-to-End Document AI Pipeline: Upload -> OCR -> Layout Analysis -> Cleaning -> Chunking -> Vector Storage.
Autonomous agents vs chatbots, planning, reasoning, tool use, agent state, execution loops, and self-correction. Tool definitions, JSON schemas, function execution, argument validation, custom tool building, and sandboxing.
Hands-on concept explanation, live coding examples, and practice exercises for Agent Fundamentals: Differences Between Chatbots & Autonomous Agents, Perception, Planning, Action & Feedback.
Hands-on concept explanation, live coding examples, and practice exercises for Autonomous Agent Architecture: System Instructions, Memory Store, Tool Registry, Planner & Execution Engine.
Hands-on concept explanation, live coding examples, and practice exercises for Agent State & Execution Loops: State Management, Dynamic Action Loops, Reflection, Self-Correction & Halting Criteria.
Hands-on concept explanation, live coding examples, and practice exercises for Tool Definitions & JSON Schemas: Defining Function Interfaces, Parameter Typing, Required Fields & Descriptions.
Hands-on concept explanation, live coding examples, and practice exercises for Function Calling Execution: LLM Tool Selection, Argument Extraction, Local Function Execution & Result Feed-back.
Hands-on concept explanation, live coding examples, and practice exercises for Building Custom Tools: Database Search, Web Search, Calculator, Email Dispatcher, Code Runner & API Connectors.
Hands-on concept explanation, live coding examples, and practice exercises for Tool Execution Security: Tool Permissions, Authorization Checks, Input Sanitization, Sandboxing & Audit Logs.
Agent workflows, ReAct pattern, router pattern, supervisor pattern, and multi-agent team orchestration. MCP architecture, Client-Server model, tools, resources, prompts, custom server implementation, and security.
Hands-on concept explanation, live coding examples, and practice exercises for Agent Workflow Architectures: State Machines, Directed Acyclic Graphs (DAGs), Human-in-the-Loop & Routing.
Hands-on concept explanation, live coding examples, and practice exercises for ReAct Pattern Implementation: Implementing Think -> Act -> Observe -> Repeat Execution Cycles.
Hands-on concept explanation, live coding examples, and practice exercises for Router Agent Pattern: Dynamic Request Classification & Dispatching to Specialized Domain Sub-Agents.
Hands-on concept explanation, live coding examples, and practice exercises for Supervisor & Hierarchical Multi-Agent Teams: Team Coordination, Task Delegation, Sub-Agent Synthesis & Reviewers.
Hands-on concept explanation, live coding examples, and practice exercises for Model Context Protocol (MCP) Fundamentals: Architecture, MCP Clients, MCP Servers, Transports (stdio, SSE).
Hands-on concept explanation, live coding examples, and practice exercises for Core MCP Primitives: Tools (callable actions), Resources (context data), Prompts (templates) & JSON-RPC.
Hands-on concept explanation, live coding examples, and practice exercises for Building Custom MCP Servers: Database MCP Server, Filesystem MCP Server, Recruitment Server & Documentation Server.
Hands-on concept explanation, live coding examples, and practice exercises for Enterprise MCP Security: Client Authentication, Authorization, Granular Tool Permissions & Audit Logging.
Hands-on concept explanation, live coding examples, and practice exercises for Memory Taxonomies for AI: Short-Term Working Memory, Conversation Buffers, Semantic Memory & Episodic Memory.
Hands-on concept explanation, live coding examples, and practice exercises for Multi-Tier Memory Storage: Storing Raw History in PostgreSQL, Session State in Redis & Semantic Memory in Vector DB.
Hands-on concept explanation, live coding examples, and practice exercises for Practical Implementation: Personalized AI Career Assistant Remembering User Skills, History & Long-Term Goals.
Text, image, audio, video, vision understanding, OCR, Speech-to-Text (STT), Text-to-Speech (TTS), and multimodal interview systems.
Hands-on concept explanation, live coding examples, and practice exercises for Multimodal Foundations: Cross-Attention Mechanisms, Aligning Vision and Language Embedding Spaces.
Hands-on concept explanation, live coding examples, and practice exercises for Vision Capabilities: Image Understanding, Visual Question Answering (VQA), Document Layout & Diagram Analysis.
Hands-on concept explanation, live coding examples, and practice exercises for Audio & Voice AI: Whisper Speech-to-Text (STT), Real-Time Text-to-Speech (TTS) & Voice Agent Architectures.
Hands-on concept explanation, live coding examples, and practice exercises for Practical Project: Multimodal AI Interview Assistant Processing Spoken Voice, Resume Documents & Verbal Answers.
Benchmark methodologies, human evaluation, LLM-as-a-judge, Ragas metrics for RAG, and agent evaluation frameworks. Token usage telemetry, inference latency, cost tracking, distributed tracing, span trees, and production monitoring.
Hands-on concept explanation, live coding examples, and practice exercises for The Need for Rigorous AI Benchmarks: Measuring Accuracy, Reliability, Consistency, Hallucinations, Cost & Latency.
Hands-on concept explanation, live coding examples, and practice exercises for Evaluation Methodologies: Rule-Based Evaluation, Human Ground Truths, LLM-as-a-Judge & Automated Eval Suites.
Hands-on concept explanation, live coding examples, and practice exercises for RAG Evaluation with Ragas: Context Precision, Context Recall, Context Relevance, Faithfulness & Answer Relevancy.
Hands-on concept explanation, live coding examples, and practice exercises for Agent Evaluation: Task Success Rate, Tool Selection Accuracy, Parameter Validation & Recovery Trajectories.
Hands-on concept explanation, live coding examples, and practice exercises for Metrics & Telemetry: Token Consumption, Inference Latency, Error Rates, Request Volumes & Model Cost Tracking.
Hands-on concept explanation, live coding examples, and practice exercises for Distributed Tracing for AI: Tracking Request -> Prompt -> Retriever -> Vector Search -> LLM -> Tool -> Response.
Hands-on concept explanation, live coding examples, and practice exercises for Production AI Monitoring: Span Trees, Request IDs, Prompt Version Telemetry & Latency/Cost Anomaly Alerting.
Hands-on concept explanation, live coding examples, and practice exercises for OWASP Top 10 for Large Language Models: Prompt Injection, Sensitive Data Exposure, Excessive Agency & Insecure Outputs.
Hands-on concept explanation, live coding examples, and practice exercises for Attack Vectors & Jailbreak Prevention: Direct/Indirect Injections, Jailbreak Heuristics & Instruction Hierarchy Enforcement.
Hands-on concept explanation, live coding examples, and practice exercises for Security Controls & Guardrails: Input Sanitization, NeMo Guardrails, Llama Guard, Output Validation & Sandboxing.
Hands-on concept explanation, live coding examples, and practice exercises for Data Protection & PII: PII Detection, Redaction, Data Masking, Encryption in Transit/Rest & Access Controls.
Hands-on concept explanation, live coding examples, and practice exercises for Token Economics & Cost Modeling: Pricing Models, Token Budgeting, Prompt Compression & Response Length Controls.
Hands-on concept explanation, live coding examples, and practice exercises for Semantic Caching with Redis: Exact Match Caching vs Embedding Semantic Caching & Cache Invalidation Strategies.
Hands-on concept explanation, live coding examples, and practice exercises for Intelligent Model Routing: Fast/Inexpensive Models for Simple Tasks, Frontier Models for Reasoning & Batching.
MockAttempt AI Career Intelligence Platform: candidate profiling, skill analysis, RAG curriculum assistant, and AI interviewer.
Hands-on concept explanation, live coding examples, and practice exercises for MockAttempt Platform Overview: Architecture of an Enterprise AI Career Intelligence & Placement Ecosystem.
Hands-on concept explanation, live coding examples, and practice exercises for Candidate Skill Evidence Engine: Unifying Resumes, GitHub Repositories, Assessments & AI Vivas into Verified Passports.
Hands-on concept explanation, live coding examples, and practice exercises for Personalized RAG Career & Learning Assistant: Semantic Indexing of Curriculum, Practice Tasks & Interview Questions.
Hands-on concept explanation, live coding examples, and practice exercises for Autonomous AI Technical Interviewer: Conducting Real-Time Socratic Vivas, Dynamic Follow-Ups & Rubric Scoring.
Hands-on concept explanation, live coding examples, and practice exercises for Recruiter AI Talent Matchmaker & Trainer Interventions: Semantic Shortlisting, Gap Analysis & Automated Coaching.
Hands-on concept explanation, live coding examples, and practice exercises for Production Deployment & Live Architectural Defense: Deploying to AWS EKS with Terraform & Defending Before Industry Panel.
Hands-On Capstone & Production Projects (2)
Build, test, and deploy real production repositories reviewed by your trainer for your engineering portfolio.
Generative AI Capstone — Enterprise RAG & Autonomous Multi-Agent Knowledge Platform with MCP
Hands-on architectural implementation built with industry clean-code standards.
Project 2 — Enterprise RAG Document Assistant
Hands-on architectural implementation built with industry clean-code standards.
🎯 Who Should Join
Python/JS developers, backend engineers, product engineers, and students building production RAG systems, AI Copilots, and Autonomous Agents.
📋 Prerequisites
Working knowledge of Python basics and REST APIs. No prior deep-learning math required.
Frequently Asked Questions
Key details about course fees, live batch schedules, syllabus depth, capstone review, and certificate verification.
What is the fee and duration for Generative AI, RAG & AI Agents Online Training? ↓
Generative AI, RAG & AI Agents Online Training has an all-inclusive fee of ₹4,999 for 6 weeks (60 hours at 10 hours/week) of live instructor-led online classes, complete module-by-module lab source code, topic mock tests, AI viva practice, and capstone mentorship.
Does Generative AI, RAG & AI Agents Online Training cover the complete syllabus and a real capstone project? ↓
Yes. You cover all 10 structured modules mastering OpenAI / Claude / Gemini APIs, Prompt Engineering, Embeddings & pgvector, Advanced Hybrid RAG, LangChain & LangGraph, and build the hands-on capstone project (Generative AI Capstone — Enterprise RAG & Autonomous Multi-Agent Knowledge Platform with MCP) with mentor code review and GitHub portfolio deployment.
How do I enquire or connect with the trainer before joining a batch? ↓
Click the "Enquire / Request Trainer Callback" button on this course page and submit your contact details. Our trainer will call you directly to walk through the complete syllabus, upcoming live batch timings, and capstone architecture.
Are topic-wise mock tests, AI interviews, and a completion certificate included? ↓
Yes. Every lesson includes 20+ calibrated topic mock tests plus live AI voice technical viva practice, and graduates receive a tamper-proof QR-verifiable Course Completion Certificate.
Enquire for Live Online Batch (₹4,999)
Get a direct call from the course trainer with upcoming batch timings, full syllabus PDF, and capstone overview.
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