AI Reasoning Models in Practice: Building an AI-Powered Coach
28mAdvanced2025-11-03
Authors
Kesha Williams
Software Engineering Manager, Speaker, Tech Blogger
Course details
AI reasoning models are changing how we interact with AI, providing more structured, logical, and multistep responses compared to traditional GPT models. In this project-based course, learn when and how to use reasoning models as you create an AI-powered personal coach. Instructor Kesha Williams—a leader in enterprise architecture and AI strategy and governance—provides hands-on challenges that allow you to apply reasoning models to decision-making, code understanding, and image-based reasoning while optimizing model efficiency. Whether you’re a developer or data scientist looking to integrate advanced reasoning models into practical solutions—or a technical decision-maker interested in the capabilities and applications of AI reasoning models—this course can help you integrate this exciting technology into real-world AI workflows.
Learning objectives
Differentiate AI reasoning models from traditional GPT models and compare leading reasoning models such as OpenAI o1 and o3, DeepSeek-R1, Gemini 2.0 Flash, and Grok 3.
Apply best practices for prompting reasoning models, including structuring prompts effectively and using meta-prompting techniques to refine AI responses.
Utilize AI reasoning models for real-world tasks, such as decision-making, code understanding, and image-based reasoning, selecting the best model for each scenario.
Manage AI reasoning models efficiently by optimizing reasoning tokens, controlling context windows, and ensuring accuracy and fairness in model outputs.
Implement hands-on techniques to integrate reasoning models into practical AI workflows.
Learning objectives
Differentiate AI reasoning models from traditional GPT models and compare leading reasoning models such as OpenAI o1 and o3, DeepSeek-R1, Gemini 2.0 Flash, and Grok 3.
Apply best practices for prompting reasoning models, including structuring prompts effectively and using meta-prompting techniques to refine AI responses.
Utilize AI reasoning models for real-world tasks, such as decision-making, code understanding, and image-based reasoning, selecting the best model for each scenario.
Manage AI reasoning models efficiently by optimizing reasoning tokens, controlling context windows, and ensuring accuracy and fairness in model outputs.
Implement hands-on techniques to integrate reasoning models into practical AI workflows.
Concepts
Introduction
- Welcome to AI Reasoning Models
Setting Up Your AI Personal Coach
- Challenge - Build the core AI personal coach
- Solution - Build the core AI personal coach
Apply Prompt Engineering for Better AI Control
- Understand how prompting differs for reasoning models
- Apply meta prompting for better AI control
- Challenge - Integrate personalization and model selection
- Solution - Integrate personalization and model selection
Build Interactive and Context-Aware Reasoning Systems
- Implement context tracking and optimization
- Challenge - Implement context management
- Solution - Implement context management
Next Steps
- Your AI reasoning journey