Episode 159: Big Lessons from Small Models with Gwyneth Peña‑Siguenza

What can small language models teach us that the largest AI models cannot?

Kelly and Julian are joined by Microsoft Cloud Advocate Gwyneth Peña-Sigüenza to explore why working with small language models (SLMs) may be one of the best ways to understand AI. Rather than relying on increasingly capable models that hide complexity, Gwyneth argues that constraints build stronger fundamentals. From prompt engineering and context management to deployment and security, SLMs force learners to think more carefully about how AI actually works.

The conversation extends beyond AI models into learning itself. Gwyneth shares her self-taught journey from growing up on a remote farm in Ecuador with limited internet access to becoming a Microsoft Cloud Advocate and creator of the Learn to Cloud platform. Along the way, the group discusses productive struggle, mentorship, cloud engineering, Python, security, and what educators should prioritize as AI becomes part of every student's learning experience.

The episode closes with a thoughtful discussion about AI dependency, judgment, and whether we would actually flip the switch and turn AI off if given the choice.

Show Notes

Wins of the Week

Gwyneth celebrates the New York Knicks reaching the NBA Finals after more than 50 years.

Julian shares that he has accepted a new role as a Fractional CTO.

Kelly reflects on taking her first real vacation in over a year—and how stepping away from work sparked unexpected ideas.

Small Language Models

Why SLMs are valuable teaching tools

Learning prompt engineering through constraints