I've started the IBM RAG and Agentic AI Professional Certificate on Coursera. The course covers Retrieval-Augmented Generation systems and agentic AI—two areas that are becoming increasingly important as we move beyond basic chatbots toward AI systems that can actually do things.

RAG combines the power of large language models with the ability to retrieve and reference external information, while agentic AI focuses on systems that can take actions, make decisions, and work autonomously toward goals. These aren't abstract concepts anymore—they're the techniques behind the AI tools we're starting to use daily.

Study Advice That Actually Makes Sense

Coursera includes some straightforward study guidance that's worth following:

Before you start:

• Scan through the module overviews to understand what's coming
• Check the time estimates and set realistic deadlines
• Schedule actual blocks of time for studying (not just "whenever I get around to it")

While learning:

• Take notes as you go—download transcripts and highlight key parts
• Complete the labs, they're where the real learning happens
• Use the glossaries, technical terms matter

Stay consistent:

• Talk about what you're learning with others (keeps you accountable and helps solidify concepts)
• Review your notes before quizzes
• If you get answers wrong, go back to the source material—don't just retry

The advice is basic but effective. The key insight: treat it like a commitment, not a casual browse. Set a timeline, stick to it, and actually engage with the material rather than passively watching videos.

I'll be documenting interesting concepts and practical applications as I work through the course. The intersection of RAG and agentic systems is where things get particularly interesting—when AI can both retrieve knowledge and take autonomous action, it opens up entirely new possibilities.