Overview
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This three-course specialization takes AI practitioners, developers, and researchers through the full lifecycle of AI agent development — from understanding intelligent agent theory to implementing and deploying autonomous, learning-driven systems. You will start by mastering core agent principles including perception, reasoning, action, decision-making, and planning across reactive, goal-based, and learning agent architectures, with hands-on implementation in Python.
As you progress, you will build autonomous agents using reinforcement learning, exploring exploration vs. exploitation strategies, reward shaping, and policy optimization through Q-Learning, DQN, and policy gradient methods. The final course brings everything together by guiding you through designing task-oriented and conversational AI agents using LLMs, integrating reasoning, memory, and tool use with LangChain and OpenAI APIs, and orchestrating multi-agent collaborative workflows. By the end, you will be able to design, train, and deploy AI agents capable of reasoning, planning, and collaborating with humans and other agents across various domains.
Syllabus
- Course 1: Agent Foundations and Prompt Engineering
- Course 2: RAG Systems & Agentic Workflows with Pinecone and LangGraph
- Course 3: Advance Multi-Agent Systems & Production AI with LangSmith
Courses
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"This Retrieval Systems, RAG, and Agentic Workflows course equips you with the skills to build intelligent AI systems that retrieve, reason, and respond using real-world data. You'll work with tools like ChromaDB, Pinecone, LangChain, LangFuse, and Python to design production-ready retrieval and agent pipelines. In Module 1, you'll explore the fundamentals of Retrieval-Augmented Generation (RAG), learning how to connect language models with external knowledge using embeddings and similarity search. Module 2 dives into vector databases and semantic search, where you'll build vector indexes, query semantic data, and evaluate performance tradeoffs across platforms like Chroma, Pinecone, and FAISS. Module 3 focuses on conversational agents and workflows — you'll design state-based dialogue systems, manage session memory, and build multi-step Q&A chatbots powered by RAG. In Module 4, you'll master optimization, debugging, and observability, using tools like LangFuse and OpenTelemetry to diagnose issues, visualize latency, and improve agent outputs through reranking and query routing. By the end of this course, you will: - Build end-to-end RAG pipelines that connect LLMs with external knowledge sources - Design and query vector databases for context-aware semantic search - Create conversational agents with memory, state management, and retrieval-driven Q&A - Debug, optimize, and monitor AI agent workflows using observability tools Disclaimer: This is an independent educational resource created by Board Infinity for informational and educational purposes only. This course is not affiliated with, endorsed by, sponsored by, or officially associated with any company, organization, or certification body unless explicitly stated. The content provided is based on industry knowledge and best practices but does not constitute official training material for any specific employer or certification program. All company names, trademarks, service marks, and logos referenced are the property of their respective owners and are used solely for educational identification and comparison purposes.
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Agent Foundations and Prompt Engineering is designed for learners eager to master the emerging field of AI agents and advanced prompt engineering. You'll learn how to design, build, and deploy intelligent AI agents using large language models (LLMs), craft high-quality prompts for various tasks, and automate complex workflows through programmatic execution and chaining. To begin with, you'll explore the fundamentals of AI agents, including their structure, behaviors, and real-world applications. You'll understand how LLMs enable agent intelligence and compare different agent architectures from reactive systems to sophisticated tool-using agents. The next module focuses on prompt engineering, where you'll learn to craft effective prompts using proven patterns like few-shot learning, chain-of-thought reasoning, and role prompting. You'll master the art of structuring prompts for optimal model performance and develop systematic evaluation strategies. In the third module, you'll advance to programmatic prompt execution and chaining. You'll build multi-step workflows, integrate Python code with LLM APIs, handle errors gracefully, and create production-ready prompt systems with proper debugging and monitoring. The final module teaches you to automate research and summarization tasks. You'll build end-to-end pipelines for collecting, processing, and summarizing information, implement both extractive and abstractive summarization methods, and evaluate outputs using comprehensive quality metrics. By the end of this course, you will confidently: • Design and implement AI agents for real-world automation and decision-making tasks • Craft effective prompts using advanced patterns and systematic evaluation methods • Build chained prompt workflows with robust error handling and programmatic control • Develop automated research and summarization systems with quality assessment frameworks Disclaimer: This is an independent educational resource created by Board Infinity for informational and educational purposes only. This course is not affiliated with, endorsed by, sponsored by, or officially associated with any company, organization, or certification body unless explicitly stated. The content provided is based on industry knowledge and best practices but does not constitute official training material for any specific employer or certification program. All company names, trademarks, service marks, and logos referenced are the property of their respective owners and are used solely for educational identification and comparison purposes.
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This Retrieval Systems, RAG, and Agentic Workflows course equips you with the skills to build intelligent AI systems that retrieve, reason, and respond using real-world data. You'll work with tools like ChromaDB, Pinecone, LangChain, LangFuse, and Python to design production-ready retrieval and agent pipelines. In Module 1, you'll explore the fundamentals of Retrieval-Augmented Generation (RAG), learning how to connect language models with external knowledge using embeddings and similarity search. Module 2 dives into vector databases and semantic search, where you'll build vector indexes, query semantic data, and evaluate performance tradeoffs across platforms like Chroma, Pinecone, and FAISS. Module 3 focuses on conversational agents and workflows — you'll design state-based dialogue systems, manage session memory, and build multi-step Q&A chatbots powered by RAG. In Module 4, you'll master optimization, debugging, and observability, using tools like LangFuse and OpenTelemetry to diagnose issues, visualize latency, and improve agent outputs through reranking and query routing. By the end of this course, you will: - Build end-to-end RAG pipelines that connect LLMs with external knowledge sources - Design and query vector databases for context-aware semantic search - Create conversational agents with memory, state management, and retrieval-driven Q&A - Debug, optimize, and monitor AI agent workflows using observability tools Disclaimer: This is an independent educational resource created by Board Infinity for informational and educational purposes only. This course is not affiliated with, endorsed by, sponsored by, or officially associated with any company, organization, or certification body unless explicitly stated. The content provided is based on industry knowledge and best practices but does not constitute official training material for any specific employer or certification program. All company names, trademarks, service marks, and logos referenced are the property of their respective owners and are used solely for educational identification and comparison purposes.
Taught by
Board Infinity