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Build 10+ real-world GenAI and Agentic AI projects to master the technology.
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The complete engineering program — from LLM fundamentals to production multi-agent systems. 25 Modules | 5 Phases | 15+ Frameworks | 8 Real Projects
5 Detailed Phases covering everything from Foundations to Production Deployment
From ANI to frontier models — the state of play
Transformers, attention, tokenization & emergent intelligence
Zero-shot, few-shot, CoT, ToT & structured output techniques
Vision, audio, video & code — unified model architectures
LoRA, QLoRA, instruction tuning — customise any model
Pinecone, Weaviate, pgvector — Semantic search fundamentals
Naive RAG → Advanced RAG → Modular RAG pipelines
Self-RAG, Corrective RAG (CRAG), GraphRAG & hybrid search
Build production RAG apps with top orchestration libraries
Evaluation, monitoring & optimising RAG at scale
ReAct loop, tool calling, memory types & cognitive architectures
Build production agents with Swarm, handoffs & guardrails
Stateful graph-based agents with checkpointing & HITL
Role-playing agent teams, crews, flows & task orchestration
Resources, Tools, Prompts & Sampling — the new agent standard
Hierarchical, peer-to-peer & event-driven agent networks
Long-term persistent and structured episodic memory stores
Build agents that write, run code & control browsers
Benchmarking, red-teaming & responsible agentic AI
Observability, tracing, cost control & production reliability
Domain-specific assistants for coding, research & business
n8n, Zapier AI & no-code agentic automation pipelines
Design AI-native SaaS — APIs, streaming, caching & costs
AWS Bedrock, GCP Vertex, Azure AI — production deployment
Physical AI, agent economies, AGI timeline & roadmap
OpenAI SDK, LangGraph, CrewAI, MCP, RAGAS, Pincone, FastAPI, Docker
Includes complete plan to transition into AI Engineering roles
Start your journey to becoming an AI expert. Limited seats available!