Overview
This lecture provides a comprehensive beginner-to-advanced overview of artificial intelligence (AI) in 2025, covering definitions, prompting, agents, AI-assisted coding ("vibe coding"), and future trends.
AI Fundamentals & Key Terms
- Artificial Intelligence (AI) — computer programs that perform cognitive tasks typical of human intelligence.
- Traditional AI (Machine Learning) powers systems like Google search and YouTube recommendations.
- Generative AI (GenAI) creates new content: text, images, audio, video, etc.
- Large Language Models (LLMs) are GenAI models like GPT (OpenAI), Gemini (Google), and Claude (Anthropic).
- Multimodal models process and produce text, images, audio, and video.
Prompting: Getting the Best from AI
- Prompting is providing detailed instructions to GenAI tools to achieve specific results.
- Two main frameworks improve prompting:
- "Tiny Crabs Ride Enormous Iguanas": Task, Context, Resources, Evaluate, Iterate.
- "Ramen Saves Tragic Idiots": Refine prompts, use clear/short sentences, try different phrasings, and add constraints.
- Providing context and examples enhances output specificity and quality.
Agents: Autonomous AI Systems
- AI agents pursue goals and perform tasks autonomously (e.g., customer service, coding).
- OpenAI lists six agent components: AI model, tools, knowledge/memory, audio/speech, guardrails (safety), and orchestration (deployment/monitoring).
- Prompting remains crucial, especially for multi-agent systems.
- Tools for building agents: Nend, Gumloop, OpenAI Agents SDK, Google ADK, Claude Code SDK.
- MCP (Anthropic) standardizes agent access to tools and knowledge (like a universal USB for agents).
- Multi-agent systems divide tasks among specialized agents for efficiency.
AI-Assisted Coding ("Vibe Coding")
- "Vibe coding"—describe what you want; LLMs implement it.
- Five-step framework: "Tiny Ferrets Carry Dangerous Code" (Thinking, Frameworks, Checkpoints, Debugging, Context).
- Always define product requirements, use known frameworks, version control (Git), methodical debugging, and provide context (mockups, screenshots).
- Coding tools range from beginner-friendly (Lovable, vZero, Bolt), intermediate (Replit, Firebase Studio), to advanced (Windsurf, Cursor, command-line tools like Cloud Code).
Emerging Trends & Future Directions
- Focus on integrating AI into existing workflows and products.
- AI-assisted coding reduces barriers for non-coders and boosts developer productivity, especially via command-line tools.
- Continued and growing emphasis on AI agents for automating and personalizing business processes.
- Learning to use advanced tools (e.g., command-line) is increasingly valuable.
Key Terms & Definitions
- Artificial Intelligence (AI) — computer systems that perform tasks requiring human intelligence.
- Generative AI (GenAI) — AI that creates new content (text, images, audio, etc.).
- Large Language Model (LLM) — GenAI model for processing and generating language.
- Prompting — crafting instructions for AI to produce desired outcomes.
- AI Agent — autonomous AI system that completes tasks/goals with minimal human input.
- Multimodal Model — AI model handling multiple input/output formats (text, image, audio, video).
- MCP — protocol standardizing agent access to tools and knowledge.
Action Items / Next Steps
- Practice prompting using the provided frameworks for better results.
- Choose and experiment with one AI chatbot (e.g., ChatGPT, Gemini, Claude).
- Explore AI-assisted coding tools suited to your experience level.
- Review product requirements documentation and version control basics.
- Consider further learning on AI agents and multi-agent system design.
- Answer the embedded assessments to test retention.