Fragmented - AI Developer Podcast
Fragmented - AI Developer Podcast

303 - How LLMs Work - the 20 minute explainer

02 February 2026 25:45 Donn Felker, Kaushik Gopal

Listen to episode

About this episode

Ever get asked "how do LLMs work?" at a party and freeze? We walk through the full pipeline: tokenization, embeddings, inference — so you understand it well enough to explain it. Walk away with a mental model that you can use for your next dinner party.

Full shownotes at fragmentedpodcast.com.

Show Notes

Words -> Tokens:

  • OpenAI Tokenizer visualizer -
    Visualize how text becomes tokens

Tokens -> Embeddings:

  • <a href="https://en.wikipedia.org/wiki/RGBcolormodel">RGB Color model - wikipedia
  • Word2Vec technique - wikipedia
    • Efficient Estimation of Word Representation -
      original Word2Vec paper by Mikolov et al.

Embeddings -> Inference:

  • <a href="https://en.wikipedia.org/wiki/Wordembedding">Word embedding
  • Temperature, Top-k, Top-p samping

Get in touch

We'd love to hear from you. Email is the
best way to reach us or you can check our contact page for other
ways.

We want to hear all the feedback: what's working, what's not, topics you'd like
to hear more on. We want to make the show better for you so let us know!

  • Contact us
  • Newsletter
  • Youtube
  • Website

Co-hosts:

  • Kaushik Gopal
  • Iury Souza

[!fyi] We transitioned from Android development to AI starting with
Ep. #300. Listen to that episode for the full story behind
our new direction.

Want to find AI jobs?

Join thousands of AI professionals finding their next opportunity

We respect your inbox. Unsubscribe at any time.

© 2026 Fragmented - AI Developer Podcast. All rights reserved.

Common Questions

Frequently asked questions

Quick answers about how DevFound's AI matching, resumes, and referrals work.

DevFound's AI Copilot ingests your profile, goals, and live job data to deliver curated matches in seconds. Every match includes a resume variant, suggested referrals, and interview prep so you can act immediately. The more feedback you provide, the sharper the Copilot becomes.

AI-led job searches shrink the hours spent sifting through boards and formatting resumes. DevFound pairs automation with your personal outreach, so you reserve energy for interviews and negotiation. Traditional networking still matters, but AI gives you a lift before you even send a message.

Modern AI roles expect comfort with production-grade code, data fluency, and practical ML tooling. The strongest candidates pair deep technical chops with storytelling—translating model impact to product, GTM, and exec partners. Continuous learning keeps you ahead as stacks evolve.

DevFound rewards active seekers. Keep your profile fresh, respond to match quality prompts, and enable alerts so you never miss a role. The AI prioritizes companies and teams that align with your feedback, accelerating both introductions and interview invites.

High-density tech hubs continue to host the deepest AI talent pools, yet distributed teams are catching up fast. Use DevFound filters to hone in on onsite, hybrid, or fully remote roles and watch openings expand across time zones.

DevFound aggregates thousands of remote AI openings and flags the nuances—core hours, async culture, and visa needs—up front. The Copilot also recommends how to position your distributed work experience so hiring managers know you can thrive on a remote team.