Agentic AI Course in Lucknow — LangChain, LangGraph & Private RAG

Har stage isi baseline par build hoti hai — running basic prompts with LLMs. Is stage me aap samajhte hain ki large language models actually kaise kaam karte hain, unhe reliably prompt kaise karte hain, aur unke outputs ko control kaise karte hain — wahi foundation jo Stage 02 (LangChain) expect karta hai.

Topics you will master

• LLM fundamentals — tokens, context windows, aur model families in plain English
• Prompt engineering basics — clear instructions, role prompts, few-shot examples
• LLM APIs se pehla kaam — keys, parameters, aur output control (length, format)
• Real tasks par practice — summarise, extract, rewrite prompts jo kaam karte hain

Step into the world of Large Language Models (LLMs) and master the industry-standard LangChain framework. This stage transitions you from running basic prompts to building stateful chatbots, connecting to databases, and deploying interactive AI applications. You will learn to work with local models on your own machine using Ollama, integrate external databases, and deploy secure applications on AWS.

Topics you will master

• LangChain v1 core concepts, PromptTemplates, and chain structures
• Ollama integration with local LLMs (Qwen3, DeepSeek, LLaMA)
• Chatbots with conversational memory and streaming responses
• RAG pipelines and text-to-MySQL database agents
• Deploying AI applications on cloud services like AWS

Take full control over your agentic workflows using LangGraph. Traditional chains are linear and fail at handling complex, iterative loops or conditional branching. This stage covers modeling LLM agents as stateful graphs, configuring nodes and edges, executing ReAct agent loops, handling human-in-the-loop validation, and building reliable, scalable agent systems that can recover from errors.

Topics you will master

• LangGraph state definitions, reducers, nodes, and routing edges
• Tool-calling agent loops (ReAct pattern) and custom tools integration
• Multi-agent architectures, nested subgraphs, and supervisor nodes
• Stateful persistence, time-travel debugging, and manual human-in-the-loop approvals
• Production deployment with streaming graph updates and error handling

Design and deploy production-grade RAG systems that prioritize absolute privacy and security. Running everything locally eliminates data leakage risks and API costs. This stage covers advanced retrieval strategies including Self-RAG and Adaptive RAG, page-level document layout parsing, metadata filtering, and constructing intelligent search agents that validate and correct their own answers before outputting them.

Topics you will master

• Fully offline, private RAG pipelines using local Ollama models
• Advanced routing architectures: Corrective RAG (CRAG), Self-RAG, and Adaptive RAG
• Structural document parsing, text chunking, and metadata extraction via Docling
• Embeddings generation, vector database search, and cross-encoder re-ranking
• Relational database connectors and custom MySQL agent configurations

The ultimate stage in agent engineering: designing federated multi-agent systems. This stage covers constructing specialized agents that collaborate with each other, share context, and execute complex, multi-modal workflows using Gemini and LangChain. Master hybrid search, Qdrant database management, parallel tool execution, memory caching, and packaging your entire workspace into Docker containers.

Topics you will master

• Advanced multi-agent architectures (collaborative, supervisor, and hierarchical networks)
• Gemini multimodal inputs (extracting tables, text, and graphics from complex documents)
• Qdrant Vector DB integration with hybrid keyword + dense search indices
• Parallel tool calling, distributed state management, and memory cache optimization
• Containerization with Docker and secure microservices deployment