Practical AI and Prompt Workflows
Move beyond conversational chat to use language models as analytical engines and structured workflow partners. Learn prompt architecture, token mechanics, schema extraction, and tool integration.
Like Duolingo, but for Practical AI and Prompt Workflows. Tomo turns the whole topic into a game you play five minutes a day, until it actually sticks.
75 levels across 9 sections, about 150 minutes end to end, roughly 30 days at five minutes a day. It moves through Elevating Everyday Prompting; Precision Prompt Engineering; Under the Hood: Tokens, Context, and Sampling; Real-World Transformation and Extraction; Chaining and Multi-Step Systems; Grounding AI in External Knowledge; Tool Use and Agentic Behaviors; Reliability, Security, and Evaluation; and Production Decisions: Cost, Latency, and Architecture. It assumes you already know the basics.
Free forever · No credit card · iPhone & Android
Key ideas in Practical AI and Prompt Workflows
- LLMs act as deterministic processors when given both explicit source data and a fixed output transformation rule.
- Querying models as general oracles triggers ungrounded retrieval from training weights rather than provided text.
- Restricting the model to operate strictly upon supplied text bounds the output and prevents made-up facts.
- Delimiters like XML tags or triple backticks create a clear boundary between operational prompt commands and raw text.
- Without structural wrappers, instructional phrases embedded inside raw data can hijack the prompt's intended task.
- Clear demarcation prevents context bleed where data is accidentally parsed as meta-instructions.
- Instructing the model to ask diagnostic questions first exposes unstated constraints before drafting begins.
- Answering targeted clarifying questions sequentially provides the missing context the LLM needs.
- Final synthesis occurs only after all diagnostic requirements have been elicited and confirmed.
- Enforcing an explicit output schema forces the model to normalize and categorize unstructured input data.
- Targeted data schemas like JSON or markdown tables eliminate conversational fluff and ensure parsing consistency.
- Specifying the target audience calibrates technical depth, tone, and assumed domain vocabulary immediately.
- Operational bounds like length limits, banned words, and exact formats prevent iterative formatting tweaks.
- Defining the evaluation criteria upfront lets the model filter its own draft before returning the response.
- Role prompts shift the model's vocabulary distribution and tone toward a technical niche.
- Role prompts cannot unlock reasoning power or eliminate underlying knowledge gaps.
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 Practical AI and Prompt Workflows 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.
What happens when you give an AI clear source text and an exact transformation rule?
Get it right to open this lesson and 74 more in the app.
Where Practical AI and Prompt Workflows takes you
- 1
Elevating Everyday Prompting
- Beyond the Chatbox: AI as an Execution Engine
- Directive Structuring and Role Framing
- 2
Precision Prompt Engineering
- Few-Shot In-Context Learning
- Deconstructing Complex Logic via Reasoning Chains
- 3
Under the Hood: Tokens, Context, and Sampling
- The Mechanics of Tokens and Context Windows
- Controlling Determinism and Creativity
- 4
Real-World Transformation and Extraction
- Unstructured Data Parsing and Synthesis
- Structured Outputs and Schema Enforcement
- 5
Chaining and Multi-Step Systems
- Sequential Prompt Chaining
- Automated Review and Critic-Refiner Loops
- 6
Grounding AI in External Knowledge
- Retrieval-Augmented Generation (RAG) Foundations
- Context Injection and Knowledge Management
- 7
Tool Use and Agentic Behaviors
- Function Calling and External Integrations
- Autonomous Agent Loops
- 8
Reliability, Security, and Evaluation
- Diagnosing and Mitigating Hallucinations
- Prompt Injections, Jailbreaks, and Defenses
- Systematic Prompt Evaluation and Regression Testing
- 9
Production Decisions: Cost, Latency, and Architecture
- Model Selection and Routing Strategies
- Enterprise AI Workflows and Governance
9 sections · 19 units · 75 levels. Built to play, not to enroll.
Tomo is in ChatGPT
Tomo is also a ChatGPT plugin: the same playable courses, built and played inside your chat.
Add Tomo to ChatGPT- What to ask
- “Make me a Tomo course on black holes.” “Turn this chat into a Tomo course.”
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- No sign-up to start. Connect your Tomo account and your streak carries on in the app.
You pick the voice
Practical AI and Prompt Workflows is taught in the Explain Like I'm 5 style: no big words. promise.. 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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