Just
₹199
A live interactive masterclass focused on Generative AI, LLMs, RAG architectures, prompt engineering, and autonomous AI agents.
Software engineering is undergoing its most profound transition in three decades. In 2026, writing raw syntax is automated—architecting autonomous multi-agent workflows, vector retrieval pipelines, and token-optimized systems is where real engineering value and career leverage are created.
Understand model latency, context windows, tokenizer budgets, and API cost structures to architect scalable LLM systems.
Master chain-of-thought prompting, schema enforcement, and tool bindings to eliminate unpredictable model responses in production code.
Connect proprietary enterprise data with semantic embeddings, hybrid keyword indexes, and cross-encoders to eliminate hallucinations.
Build autonomous agent loops that call external tools, execute code safely, and coordinate across multi-agent swarms.
Practical, industry-aligned engineering skills you can immediately apply to real-world software systems.
Understand modern foundation models, streaming APIs, and token-efficient architectures for production software.
Learn retrieval, dense embeddings, vector indexing, and knowledge synthesis workflows for enterprise databases.
Understand stateful planning, tool calling, dynamic decision loops, and autonomous multi-agent coordination.
Turn high-level AI concepts into practical, reliable, production-grade applications with clean starter repositories.
Designed for builders who want practical, code-level mastery of LLMs and autonomous agents rather than high-level non-technical overviews.
Engineers with backend, frontend, or full-stack experience wanting to build LLM-powered applications.
Python, JavaScript, and Java developers looking to master LangChain patterns, API integrations, and agent loops.
Data engineers and architects evaluating vector infrastructure, retrieval pipelines, and enterprise AI feasibility.
Founders building AI-first products who require scalable, robust software architecture rather than fragile prototypes.
Tech professionals looking to future-proof their careers and transition into high-leverage AI engineering roles.
A code-driven masterclass engineered to bridge the gap between simple API calls and production software systems.
Transformer mechanics, token budgeting, context windows, and API pricing models.
Chain-of-thought, few-shot prompting, and JSON schema enforcement for reliable software integration.
Semantic search pipelines with vector embeddings, hybrid indexing, and cross-encoder rerankers.
Designing stateful agent loops, tool calling, API executions, and validator loops.
Learning Generative AI should not be about memorizing superficial buzzwords. My focus is on clear explanations of complex AI concepts, practical coding demonstrations, and hands-on understanding.
During the masterclass, we dissect real-world architectures, explore how vector search handles enterprise data, and build autonomous agents step-by-step with clean Python code.
Your starting point for building real-world AI applications. Reserve your seat today for the live interactive cohort.
Join instructor Yagna Akkisetty for this live 3-hour deep-dive. Limited cohort seats available.