AI Workflows in Practice
Like Duolingo, but for AI Workflows 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 3 sections, about 48 minutes end to end, roughly 10 days at five minutes a day. It moves through Execution Fundamentals; Core Workplace Deliverables; and Systems, Workflows, and Quality Control. It assumes you already know the basics.
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Key ideas in AI Workflows in Practice
- Role anchors the tone, perspective, and domain vocabulary
- Context provides user background that eliminates generic filler
- Format defines exact output architecture before generation
- Few-shot exemplars visually anchor patterns better than rule text
- Language models replicate demonstrated patterns over abstract rules
- Two or three input-output pairs enforce edge-case handling
- Delimiters like XML tags create hard boundaries around raw data
- Explicit container tags stop models treating input text as commands
- Prompt injection occurs when raw data bleeds into instructions
- Negative constraints leave an unbounded void of alternatives
- Positive replacement rules provide concrete target distributions
- Pure prohibitions increase hallucination and constraint violation
- Subjective length words like 'brief' fail to curb verbosity
- Exact count limits force prioritization over conversational filler
- Strict header templates prevent spontaneous intro and outro fluff
- Tone adjectives like 'punchy' or 'professional' produce vague, generic outputs from LLMs.
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 AI Workflows 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 building a prompt, what is the primary benefit of including background context?
Get it right to open this lesson and 23 more in the app.
Where AI Workflows in Practice takes you
Move beyond casual chat and turn generative AI into a dependable daily engine. Learn concrete prompting architectures, document synthesis methods, and multi-step workflows with real workplace examples.
- 1
Execution Fundamentals
- Precision Prompting for Daily Tasks
- Style Matching and Tone Calibration
- 2
Core Workplace Deliverables
- Interrogating and Synthesizing Documents
- Data Cleanup and Structured Extraction
- Stress-Testing and Critical Evaluation
- 3
Systems, Workflows, and Quality Control
- Chaining Multi-Step Workflows
- Building Reusable System Prompts and SOPs
- Hallucination Audits and Verification Protocols
3 sections · 8 units · 24 levels. Built to play, not to enroll.
You pick the voice
AI Workflows in Practice is taught in the The Professor style: clear, structured, thorough. 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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