Yes, And…
an improv performer builds an entire scene, word by word, from nothing but what came before — and that's a surprisingly accurate picture of how a large language model actually works.
Part 1
Part 2
What the Performer Is Actually Doing
the real difference between a model that genuinely understands what it's saying and one that's producing remarkably convincing text through pattern prediction alone.
Part 3
Reciting From a Script
why rule-based language systems, however carefully written, could never handle a genuinely open-ended scene the way modern LLMs routinely do.
Part 4
Learning Every Scene Ever Performed
what actually happens during pretraining — the process of exposing a model to enormous amounts of text and letting it absorb the patterns of language itself.
Part 5
Breaking a Scene Into Beats
how a language model actually breaks text down into tokens before processing it, and why a token isn't quite the same thing as a word.
Part 6
The Performer's Instincts
what parameters actually are, and why a model with more of them isn't smarter in any simple sense — it just has more capacity to learn nuanced patterns.
Part 7
What's Being Said Right Now
why an improv performer can only genuinely respond to the scene as it's unfolded so far, and what that means for an LLM's context window.
Part 8
Which Earlier Line Actually Matters
how the attention mechanism lets a model weigh some earlier words far more heavily than others when deciding what comes next — the real breakthrough behind modern LLMs.
Part 9
How Wild the Improv Gets
how temperature controls whether a model plays it safe with the most predictable next word or takes genuine creative risks.
Part 10
A Quick Warm-Up Before the Scene
how showing a model a few examples right in the prompt lets it pick up a task's pattern on the spot, without ever changing its underlying weights.
Part 11
Confidently Inventing a Detail That Contradicts the Scene
why an LLM can state something false with exactly the same fluent confidence as something true, and why that follows directly from how it actually works.
Part 12
Coaching the Performer for a Specific Style
how fine-tuning permanently adjusts a model's weights for a specific task or style, and how that's genuinely different from just prompting it well.
Part 13
The Audience's Applause Shapes the Next Show
how RLHF uses human preference judgments, rather than just predicting the next word, to shape a model into something more genuinely helpful and aligned.
Part 14
A Troupe, Not a Solo Act
why some capabilities only show up once a model gets large enough, appearing suddenly rather than gradually — and what that means for predicting what comes next.
Part 15
When the Performer Breaks Character
how carefully crafted prompts can manipulate a model into ignoring its own intended guardrails, and why this remains a genuinely hard problem to fully solve.
Part 16
The Understudy Who Learned Fewer Scenes
why a smaller, more focused model trained on a distilled version of a larger one can still handle most real-world tasks at a fraction of the cost.
Part 17
Reading From Cue Cards Held by the Crew
a preview of how giving a model real-time reference material, rather than relying purely on memorized training, directly addresses several of this series' core limitations.
Part 18
Knowing the Performer's Limits
an honest accounting of what large language models genuinely still struggle with, even the most capable ones — because knowing the limits is as useful as knowing the capabilities.
Part 19
The Whole Theater Company
a preview of the broader ecosystem — embeddings, vector stores, agents — that turns a single capable performer into a genuinely complete production.
Part 20
Final Bow
reassembling the whole performance, from a single improvised line to a complete, honest picture of how large language models actually work.