Reasoning Models in Practice

A practical course on reasoning-class models: what extended thinking buys you, when it is worth the cost and latency, and how to prompt them well. Learners compare standard and reasoning models on real tasks.

What this course covers

The course runs across 7 topics, each one a short adaptive session rather than a recorded lecture. The tutor explains the idea, works an example, checks understanding, and adjusts the next step to the answer given.

Under the Hood: How Reasoning Models Differ — Understand the paradigm shift of extended thinking. Learn how reasoning models generate internal chains of thought before responding, and how this differs from standard token-by-token generation.
The Economics of Reasoning: Cost and Latency — Analyze the trade-offs of reasoning models. Learn about reasoning tokens, how they impact cost and API billing, and how to manage the increased latency associated with extended thinking.
Decision Matrix: When to Use Reasoning Models — Evaluate real-world use cases using a decision framework. Determine when to route tasks to reasoning models (e.g., complex logic, math, coding) versus standard models (e.g., summarization, creative writing).
Prompting Mechanics for Extended Thinking — Master prompting techniques tailored for reasoning models. Learn why traditional techniques like 'think step-by-step' are redundant, and how to focus instead on clear constraints, context, and goal definition.
Hands-on Lab: Side-by-Side Model Evaluation — Compare standard and reasoning models on identical, complex prompts in real-time. Analyze differences in accuracy, reasoning paths, execution time, and output quality directly within the tool.
Scenario Exercise: Integrating Reasoning in Workflows — Navigate a simulated business scenario to design a hybrid workflow, making critical decisions on where to deploy reasoning models to balance cost, speed, and accuracy.
Hands-on Lab: Debugging and Logic Optimization — Use a reasoning model to debug a complex script or solve a multi-step logic puzzle. Practice iterating on prompts to guide the model's internal reasoning toward the correct solution.

Skills you build

Results are measured against named skills in the OneRange taxonomy of more than 10,000 skills, so a manager sees proficiency per skill rather than a completion tick.

Who it is for

Power users, builders. The material is pitched at intermediate level, and takes roughly 90 minutes at a typical pace. Because every session adapts, someone who already knows a topic moves through it quickly instead of sitting through an explanation they do not need.

It sits in the Category: LLMs track of the Vero AI Catalog, and can be assigned to one person, a team, or the whole company.

How it is delivered and assessed

Delivery is conversational and interactive, including branching scenario, guided lab, code lab exercises. There is no video to sit through and no slide deck to click past.

Understanding is checked with a lab assessment of 5 items, with a pass mark of 70%.

Administrators can copy this course into their own library and adapt it — edit the outline, change the duration, swap the assessment format, or ground it in internal documentation so answers cite the company's own source material.

Also in this track

Large Language Models Deep Dive — This course gives a deeper understanding of how large language models work so learners can use and choose them well. It covers architecture and tokens, training and alignment, key parameters, and model selection tradeoffs. Participants finish able to make informed decisions about which model to use and why.
Small & Efficient Models — This course covers the economics and technique of small models: what they can genuinely do, distillation concepts, on-device deployment, and routing between small and frontier models by task.
Model Engineering & Customization Pathway — Choose, run, and customize models beyond prompting.

Frequently asked questions

How long does this course take?

It runs roughly 90 minutes at a typical pace. Because every session adapts, someone who already knows a topic moves through it quickly instead of sitting through an explanation they do not need.

How is it assigned to a team?

An administrator assigns it from the Vero dashboard to one person, a team, a department or the whole company, and sees progress and results per person and per skill.

Can we customise it with our own documents and terminology?

Yes. Administrators can copy this course into their own library and adapt it — edit the outline, change the duration, swap the assessment format, or ground it in internal documentation so answers cite the company's own source material.

Course code CAT-RSN.