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Build AI agent for code generation
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Module 1
Foundations of AI Agents and LLMs
How LLMs Generate Text: Tokens, Context, and Probability
Explain how LLMs generate text using tokens, context, and probability.
LLM Context Windows: Role & Limitations
Describe the role and practical limitations of the context window in LLMs.
3
Prompt Engineering for Improved LLM Outputs
Apply prompt engineering techniques (e.g., few-shot, chain-of-thought) to improve LLM outputs.
AI Agents: Beyond Chatbots
Define an AI agent and differentiate it from a simple chatbot by describing its perceive-plan-act cycle.
5
Agent Capabilities: Understanding Tool Use (Function Calling)
Explain the concept of 'tool use' (function calling) as a method for extending agent capabilities.
6
Agent Orchestration Patterns: ReAct vs. Plan-and-Execute
Compare common agent orchestration patterns like ReAct and Plan-and-Execute.
Agent Error Handling & Fallbacks
Describe strategies for error handling and fallbacks within an agent's operational loop.
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Retrieval-Augmented Generation (RAG) Principles