Building AI Apps & Agents

Explore essential development environments, frameworks (e.g., LangChain, LlamaIndex conceptually), and APIs for building modern AI applications. Understand the ecosystem of tools available for practical AI development, focusing on rapid prototyping and experimentation.

• Setting Up Your Python Environment and IDE (11 questions)
• Mastering Virtual Environments for AI Projects (13 questions)
• Installing Core AI/ML Libraries and Frameworks (24 questions)

Learn to effectively interact with Large Language Model APIs, implement advanced prompt engineering techniques for various tasks, and handle input/output for applications like text generation, summarization, and classification. Master techniques for reliable and steerable LLM outputs.

• Foundations of LLM APIs and Integration Patterns (23 questions)
• Advanced Prompt Engineering for Control and Reliability (14 questions)
• Integrating LLMs for Specific Use Cases and Tooling (25 questions)
• Evaluation, Safety, and Ethical Considerations in LLM Applications (24 questions)

Dive into Retrieval Augmented Generation (RAG) to connect LLMs with external data sources and knowledge bases. Explore function calling and tool use patterns, enabling LLMs to interact with APIs, databases, and other services to perform actions beyond pure text generation.

• Introduction to Augmenting LLMs and Retrieval-Augmented Generation (RAG) (24 questions)
• Advanced Retrieval Techniques and Vector Databases for RAG (26 questions)
• Empowering LLMs with Tools and Function Calling (11 questions)
• Building Advanced LLM Agents: Orchestration and Multi-step Reasoning (25 questions)

Understand the fundamental principles of AI agents, including memory management, planning, and reasoning. Learn to design agent architectures capable of multi-step problem-solving, making decisions, and adapting to dynamic environments.

• Introduction to Intelligent Agents (263 questions)
• Environment Perception and State Representation (29 questions)
• Agent Architectures and Action Selection (15 questions)
• Learning and Adaptation in AI Agents (26 questions)

Apply agentic principles to develop sophisticated autonomous agents that can achieve complex goals, learn from interactions, and specialize in specific domains or tasks. Explore concepts like human-in-the-loop agents, multi-agent systems, and self-correction mechanisms.

• Foundations of Advanced Agent Design & Architectures (12 questions)
• Specializing Agents with Domain-Specific Knowledge & Tools (19 questions)
• Advanced Reasoning, Planning, and Decision-Making (31 questions)
• Orchestration, Collaboration, and Evaluation of Agent Systems (11 questions)

Learn strategies for deploying AI applications and agents to production environments, including cloud services and containerization. Understand methods for evaluating their performance, monitoring behavior, and implementing best practices for ethical considerations, safety, and maintenance in real-world scenarios.

• Deployment Strategies and Operationalizing AI (24 questions)
• AI Model Evaluation, Testing, and A/B Experimentation (24 questions)
• Identifying and Understanding Responsible AI Challenges (21 questions)
• Mitigating Responsible AI Risks and Ensuring Compliance (16 questions)