Large Language Models in Practice
Like Duolingo, but for Large Language Models in Practice. Tomo turns the whole topic into a game you play five minutes a day, until it actually sticks.
For the part of you with thirty open tabs that never became anything.
24 levels across 4 sections, about 48 minutes end to end, roughly 10 days at five minutes a day. It moves through Precision Control, The Mechanics of Meaning, Customization and Integration, and Reliability and Safety. It assumes you already know the basics.
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Key ideas in Large Language Models in Practice
- Few-shot examples provide a template that the LLM mimics for formatting and tone
- The model uses the relationship between input and output in examples to infer the desired logic
- Chain-of-Thought forces the model to calculate intermediate steps before committing to a final token
- Externalizing reasoning reduces the likelihood of the model hallucinating a quick but wrong answer
- How few-shot examples establish a pattern for the LLM to follow
- The logical flow of a Chain-of-Thought prompt
- Delimiters act as boundaries that tell the LLM where instructions end and data begins
- Structured formats like JSON allow software to reliably extract specific data fields from a response
- Clear separation prevents the LLM from executing commands hidden inside user-provided text
- LLMs can be forced into valid syntax by providing a schema or a one-shot JSON example
- The function of different delimiters in preventing prompt injection
- LLMs often struggle with negation because 'not' is a single token that can be easily overlooked
- Providing a positive alternative gives the model a clear high-probability path for the next token
- The necessity of structured output for programmatic integration
- The effectiveness of positive paths over negative constraints
- System messages act as a persistent instruction layer that overrides user-level prompts.
You've tried the other tabs
Thirty open tabs. Four facts you actually kept.
You watched. You nodded. By Sunday it was gone.
One answer, then back to scrolling.
Eight weeks. You meant to finish. You didn't.
Tomo gives Large Language Models in Practice the Duolingo treatment: levels, streaks, and quick quizzes that test what you just learned. That game loop is what the tabs above never had, so it's the one you actually finish.
Here's what playing it feels like
A real question from this course. Take your best guess.
When you use few-shot prompting, how does the LLM determine the 'logic' of your request?
Get it right to open this lesson and 23 more in the app.
Where Large Language Models in Practice takes you
Move beyond basic prompting to understand the architecture, optimization, and integration of modern LLMs into real-world applications.
- 1
Precision Control
- Structuring for Predictability
- Steering the Model's Persona
- 2
The Mechanics of Meaning
- The Transformer Architecture
- Tokens and Context Windows
- 3
Customization and Integration
- Retrieval Augmented Generation (RAG)
- Fine-tuning and Adaptation
- Agents and Tool Use
- 4
Reliability and Safety
- Managing Hallucinations and Bias
4 sections · 8 units · 24 levels. Built to play, not to enroll.
You pick the voice
Large Language Models in Practice is taught in the The Bestie style: your friend who just gets it. Want a different feel? In the app you can spin up the same topic in any of Tomo's teaching styles. Same facts, totally different vibe.
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