🎓 Module 2 Complete – Building Prompt Engineering Foundations

Today I completed Module 2 of the Prompt Engineering for ChatGPT course by Vanderbilt University on Coursera.

While the material wasn’t entirely new to me, it was still a valuable experience. The structure helped me revisit essential ideas, clarify terminology, and reframe familiar practices in a more systematic way.

This module covered:

• What prompts are and how they work

• Prompt patterns, especially the “Persona Pattern”

• Introducing new information to the LLM

• Prompt length limitations and reusability

• Root prompts and few-shot prompting

All exercises were graded by AI, and I received 100% scores, which I take as a sign that I’m on the right track. It’s early in the course, so I expect more depth in the upcoming modules.

Even though I didn’t encounter anything dramatically new, this has been a useful chance to consolidate what I already know and prepare for more advanced topics ahead.

Still aiming to finish the certification before our upcoming family trip to Japan 🇯🇵 in May — staying on track so far.

🧠 First Steps Into My AI Prompt Engineering Certification Journey

Today was a busy day, but I still made some meaningful progress toward my new goal: earning my AI Prompt Engineering certification from Vanderbilt University via Coursera.

I officially started Module 1 and completed the Course Overview section. The final task of this segment was setting up a ChatGPT account — something I already had, so it was a quick win.

First impressions?
I’m genuinely impressed. The instructor is highly knowledgeable, speaks clearly, and the course structure is extremely user-friendly. I particularly appreciate the technical features:

• I can adjust the video playback speed (hallelujah for efficiency!).

• There’s a live transcript under each video, and the part currently being spoken is highlighted in green — an excellent tool for focus and comprehension.

Even though I couldn’t complete the full module today due to other responsibilities, I’m very encouraged by this start. The course feels well-designed, professional, and absolutely aligned with my goals.

I can’t wait to dive deeper into the material tomorrow and keep progressing toward my certification before our upcoming Japan trip! 🇯🇵

📚 Preparing for the Future: Learning AI Prompt Engineering Before My Japan Trip

🧩 A New Direction: Learning to Become an AI Prompt Engineer

Earlier today, a story on Google Discover caught my attention — it discussed the growing importance of AI Prompt Engineers and how this profession is becoming critical for the future of AI.

Intrigued, I decided to initiate a deep research project.
While I was resting, my ChatGPT Pro assistant was working for me in the background, analyzing different options and helping me identify the two best certifications available:

• 🏆 Best Paid Certification: Prompt Engineering Specialization (Vanderbilt University via Coursera)

• 🆓 Best Free Certification: ChatGPT Prompt Engineering for Developers (DeepLearning.AI & OpenAI)

Following this, I created my Coursera account, enrolled in audit mode, and registered for the Vanderbilt Specialization.

In parallel, I’m planning a family trip to Japan toward the end of May — and I have now set myself a clear target:

🎯 Obtain my AI Prompt Engineering certificate before the trip begins!

The journey continues, and the destination looks exciting. Challenge accepted.🚀

🌏 Google’s Notebook LM: My New Travel and Research Copilot

It’s been a while since my last update—and for good reason. I recently returned from a family trip to London, England, and have been focusing on recovery after my second Total Hip Arthroplasty (THA). But yesterday, April 19, 2025, something reignited my curiosity and motivation in a big way.

While browsing YouTube, I came across a video by Tiago Forte introducing Notebook LM, Google’s latest AI-powered tool. Here’s the link. The feature that truly caught my attention? Multimodal input support.

I’ve had disappointing experiences trying to feed multiple types of documents and formats to AI models like ChatGPT or Gemini. Context gets lost. Formats get ignored. Answers? Almost never accurate.

But Notebook LM? This one felt promising—and I had the perfect test cases ready.

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✈️ Testing It Out: Planning Our Japan Trip

We’re heading to Japan next month as a family, and I had already gathered a wealth of info:

• Travel tips

• Cultural notes

• Tech shopping guides

• YouTube videos, PDFs, website links, and copy-pasted summaries from Perplexity.ai

In total, 11 different source types. I asked Notebook LM to help me plan everything—from a visit to the historic Kodokan (the mecca of Judo) to exploring electronics markets and deciding what gear to bring back home.

The result? Incredibly well-structured, clear, and surprisingly aware of context across sources.

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🧠 Putting It to a Harder Test: A Multi-Tab Google Doc

Next, I uploaded a multi-tab Google Doc, several pages long, filled with notes across unrelated sections and Tabs. I asked questions that required cross-referencing info from different tabs—something ChatGPT and Gemini 2.0 have failed at before miserably.

Notebook LM answered accurately every single time.

It didn’t just summarize. It synthesized. It understood what I meant and where to look, even in scattered, non-linear info.

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🎧 Audio Overviews & Real-Time Q&A

One unexpected feature I found impressive was the audio overview mode, where Notebook LM creates a podcast with two speakers going over the info and discuss about it like in a podcast! But even more unique—you can interrupt the speakers with live questions, and they will adjust and answer you mid-podcast.

While I’m primarily a visual learner, the potential for this is enormous—especially in accessibility, mobility, and multitasking.  (Unbelievable learning tool for the auditory learners or the visually impaired)

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🚀 Final Thoughts

Notebook LM has opened up entirely new possibilities for how I manage knowledge. I’m already thinking about how I can apply it to:

• Course notes

• Archiving AI experiments

• Travel itineraries

• Research projects

• Structured learning

It’s early days still, but I have to say: Google has truly broken new ground here.

I’ll continue testing and documenting new use cases as I go, especially as I integrate it with my ongoing AI studies.