Step-by-Step Reasoning: Advanced — Chain-of-Thought Recipes

An advanced recipe set for eliciting reliable reasoning: decomposition, explicit steps, and built-in verification. Learners apply the recipes to problems where a single-shot answer fails.

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.

The Limits of Single-Shot Prompting — Analyze why LLMs fail on complex reasoning tasks when prompted for direct answers, and identify the cognitive transition to Chain-of-Thought (CoT).
Step-by-Step Elicitation Fundamentals — Learn the mechanics of eliciting explicit reasoning paths using system instructions, few-shot exemplars, and explicit 'think step-by-step' directives.
Guided Lab: Implementing Basic Chain-of-Thought — Hands-on lab in the tool: Refactor a failing single-shot prompt into a successful step-by-step reasoning prompt using a provided evaluation dataset.
Problem Decomposition Techniques — Explore strategies to break down monolithic, complex problems into structured, sequential sub-tasks that the model can solve incrementally.
Guided Lab: Decomposing Complex Logic Tasks — Hands-on lab in the tool: Design a prompt that forces the LLM to segment a complex mathematical or logical problem into discrete, sequential sub-problems.
Built-in Verification Steps — Study verification recipes that instruct the model to cross-check its own intermediate outputs and self-correct before presenting a final answer.
Guided Lab: Designing Self-Verifying Reasoning Loops — Hands-on lab in the tool: Create and test a robust, self-verifying prompt recipe that detects and corrects its own calculation errors in real-time.

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, all functions. The material is pitched at advanced 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 Prompt Recipes & Playbooks 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 guided 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

All Prompting training courses →
Context Engineering: Advanced — Memory & Token Budgeting — This course teaches builders to design what goes into the context window to maximize reliability and relevance. It covers context windows, retrieval versus stuffing, memory patterns, summarization, and token budgeting. Learners finish able to get more accurate output by controlling context deliberately.
Getting Answers in a Fixed Format: Advanced — Structured-Output Recipes — Recipes for output that machines can consume: format contracts, schema-shaped prompts, and robustness tricks that survive model updates. Learners harden a prompt until its output parses every time.
Prompt Libraries for Teams: Advanced — Building & Governing — The organizational layer of prompting: curating, storing, and governing a shared prompt library. Learners stand up a real library structure with contribution standards and an upkeep loop.
Prompt Debugging: Advanced — Iteration & Versioning — When a prompt fails, most people guess; this course teaches diagnosis. Learners isolate failure causes, iterate systematically, and version what works.
Advanced Prompt Engineering — Learners master advanced prompting techniques for reliable, structured results. The course covers few-shot and chain-of-thought, structured output, system prompts and roles, and prompt chaining and templates. They finish able to engineer prompts that perform consistently at scale.
Prompting Proficiency Assessment — Prompting Proficiency: can the person get a usable, correctly formatted answer on the first or second try?
Prompt Engineer Pro — Complete command of prompt technique and team practice
Getting Step-by-Step Answers Pathway — Move from asking for step-by-step answers to designing full reasoning recipes.

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 PRC-COT.