Module 17 Optimizing Token Usage in RAG The Power of Contextual Compression

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Students will embark on a comprehensive journey through the entire lifecycle of Agentic AI development. The journey begins with the Evolution of Agency, where learners will understand the shift from simple, prompt-based chatbots to fully autonomous agents capable of independent reasoning. They will gain an expert-level understanding of the LangChain Ecosystem, learning how to leverage Core, Community, and Partner packages to build modular applications. A significant portion of the learning is dedicated to the LangChain Expression Language (LCEL), where the principles of declarative chains are taught to enable the creation of highly readable and maintainable AI workflows. Students will also master the art of Prompt Engineering using Dynamic Inputs and Few-Shot Selectors to steer LLMs with surgical precision.

As the course progresses into LangGraph, students will learn how to design cyclic architectures using Nodes, Edges, and State. This includes mastering StateGraph Fundamentals and implementing advanced patterns like Time Travel and Persistence for stateful agents. The curriculum dives deep into the world of Multi-Agent Systems, teaching students how to implement Supervisor Patterns and Hierarchical Teams to solve complex, multi-faceted problems. Beyond architecture, students will learn critical production skills, including Evaluation with LangSmith, Monitoring for Tool Latency, and implementing Security Guardrails against prompt injection. By the end of the 65 modules, students will not only know how to build an agent but how to design a self-correcting, resilient, and production-ready agentic ecosystem that is future-proofed against the next wave of AI evolution.

All Courses available at : https://interview.quicktechie.com/tra...