GenAI Skills Academy with CodeSignal

Equip every team with practical GenAI skills that make an impact. With role-aligned learning tracks, your teams learn how to use, integrate, and create with GenAI on-the-job—without putting core responsibilities on hold.

Upskill your teams with a flexible, role-based approach to GenAI learning

CodeSignal’s GenAI Skills Academy gives your teams hands-on, role-specific training in just a few hours per week. Whether you’re enabling broad adoption or developing deep technical talent, each track combines structured learning with practical tools to make GenAI part of everyday work.

Explore all three learning tracks

Each track is built around weekly, hands-on modules that focus on real tools and real outcomes. Employees learn just a few hours per week while staying focused on their day-to-day work.

Track 1: AI Use

Understand and learn skills to use the latest AI tools and capabilities to be more effective at work and help drive the company forward in the AI age.

Best for
All employees

Prerequisites
None

Key Skills
GenAI capability awareness, GenAI proficiency and use, GenAI limitation awareness

Outcomes
Employees graduating from this track should have the skills and expertise to use AI effectively and responsibly in their day-to-day job to unlock new levels of productivity.

SCROLL 👉

Content	Primary Target Skills	Category	Period
Generative AI in 2025 – Overview and Practice	GenAI capability awareness	Knowledge, practice	WEEK 1
Mastering Communication with AI Language Models	GenAI proficiency and use	Knowledge, practice	WEEK 2
Applying Generative AI in Everyday Professional Tasks	GenAI proficiency and use	Knowledge, practice	WEEK 3
Making Things Shine – Practice and Learn Image Generation with AI	GenAI capability awareness	Knowledge, practice	WEEK 4
Generative AI – The Next Frontier: Voice, Video, and More	GenAI limitation awareness	Knowledge, practice	WEEK 5
AI Literacy Assessment	GenAI capability awareness, GenAI proficiency and use	Certification	WEEK 6
Understanding LLMs and Basic Prompting Techniques	Prompt design and development	Knowledge, practice	WEEK 7
Engineering Output Size with LLMs	Prompt design and development	Knowledge, practice	WEEK 8
Journey into Format Control in Prompt Engineering	Task analysis and outcome definition	Knowledge, practice	WEEK 9
Prompt Engineering for Precise Text Modification	Prompt testing and iteration	Knowledge, practice	WEEK 10
Advanced Techniques in Prompt Engineering	Advanced prompting techniques	Knowledge, practice	WEEK 11
Prompt Engineering Assessment	Prompt design and development, prompt testing and iteration, advanced prompting techniques	Certification	WEEK 12

Track 2: AI Integration

Master skills to effectively integrate foundational models across various GenAI categories (text generation, image generation, multi-media generation, etc.) into the company’s products/services.

Best for
Engineers

Prerequisites
Foundational and essential skills in software development.

Key Skills
Prompt design and development, prompt testing and iteration, GenAI capability awareness, GenAI proficiency and use, GenAI limitation awareness, RAG for large language models

Outcomes
Employees graduating from this track should have the skills and expertise to collaborate with and use AI tools effectively to bring more innovation to the company’s products.

SCROLL 👉

Content	Target Skills	Category	Period
Understanding LLMs and Basic Prompting Techniques	Prompt design and development	Knowledge, practice	WEEK 1
Engineering Output Size with LLMs	Prompt design and development	Knowledge, practice	WEEK 2
Journey Into Format Control in Prompt Engineering	Task analysis and outcome definition	Knowledge, practice	WEEK 3
Prompt Engineering for Precise Text Modification	Prompt testing and iteration	Knowledge, practice	WEEK 4
Advanced Techniques in Prompt Engineering	Advanced prompting techniques	Knowledge, practice	WEEK 5
Prompt Engineering Assessment	Prompt design and development, prompt testing and iteration, advanced prompting techniques	Certification	WEEK 6
Customer-Led Live Training (Cursor, GitHub Copilot, Windsurf)	GenAI-assisted development	Live training	WEEK 7
Introduction to RAG	RAG in large language models	Knowledge, practice	WEEK 8
Text Representation Techniques for RAG Systems	Feature engineering and text representation	Knowledge, practice	WEEK 9
Scaling up RAG with Vector Databases	Feature engineering and text representation	Knowledge, practice	WEEK 10
Beyond Basic RAG: Improving our Pipeline	Programming and text processing algorithms	Knowledge, practice	WEEK 11
Creating a Chatbot with OpenAI in Python	GenAI capability awareness	Knowledge, practice	WEEK 12
Building a Chatbot Service with Flask	GenAI integration	Knowledge, practice	WEEK 13
Developing a Chatbot Web Application With Flask	GenAI integration	Knowledge, practice	WEEK 14
AI-Assisted Coding Assessment	GenAI-assisted development, GenAI proficiency and use	Certification	WEEK 15

Track 3: AI Creation

Understand the foundations of deep learning and be able to translate cutting edge AI research into functional software. Also includes skills to pre- and post-train AI models, handle large scale data engineering, and model deployment.

Best for
Aspiring AI Researchers

Prerequisites
Machine Learning Foundations Qualification Assessment. Significant level of foundational skills in mathematics, data algorithms, data engineering, and basic data science.

Key Skills
Advanced mathematics and AI algorithms, text data collection and preparation, machine learning modeling for NLP, Deep learning for NLP, Large scale data collection and preparation

Outcomes
Employees graduating from this track should have the skills and expertise necessary to be hired through the standard interview process into the AI Researcher role at the company.

SCROLL 👉

Content	Primary Target Skills	Category	Period
Machine Learning Foundations Assessment	Mathematics and data algorithms	Qualification	WEEK 1-2
Regression and Gradient Descent	Machine learning model development	Knowledge, Practice	Week 3-4
Classification Algorithms and Metrics	Machine learning model development	Knowledge, practice	WEEK 5-6
Gradient Descent: Building Optimization Algorithms from Scratch	Coding and data algorithms	Knowledge, practice	WEEK 7-8
Ensemble Methods from Scratch	Machine learning model development	Knowledge, practice	WEEK 9-10
Unsupervised Learning and Clustering	Machine learning model development	Knowledge, practice	Week 11-12
Neural Networks Basics from Scratch	Deep learning and neural networks	Knowledge, practice	Week 13-14
Introduction to PyTorch Tensors	Deep learning and neural networks	Knowledge, practice	WEEK 15-16
Building a Neural Network in PyTorch	Deep learning and neural networks	Knowledge, practice	Week 17-18
Modeling the Wine Dataset with PyTorch	Deep learning and neural networks	Knowledge, practice	WEEK 19-20
PyTorch Techniques for Model Optimization	Model validation and selection	Knowledge, practice	Week 21-22
Introduction to Text Data Exploration in Python	Text data collection and preparation	Knowledge, practice	WEEK 22-23
Text Data Preprocessing in Python	Text data collection and preparation	Knowledge, practice	WEEK 23-24
Introduction to TF-IDF Vectorization in Python	Feature engineering and text representation	Knowledge, practice	WEEK 25-26
Building and Evaluating Text Classifiers in Python	Machine learning modeling for NLP	Knowledge, practice	WEEK 27-28
Collecting and Preparing Textual Data for Classification	Text data collection and preparation	Knowledge, practice	WEEK 29-30
Feature Engineering for Text Classification	Feature engineering and text representation	Knowledge, practice	WEEK 31-32
Introduction to Modeling Techniques for Text Classification	Machine learning modeling for NLP	Knowledge, practice	WEEK 33-34
Advanced Modeling for Text Classification	Machine learning modeling for NLP	Knowledge, practice	WEEK 35-36
AI Researcher Assessment	Machine learning model development, coding and data algorithms	Certification	WEEK 37-38

Unlock GenAI skills at scale

Accelerate AI readiness

Rapidly build AI proficiency at every level with targeted, hands-on learning tracks.

Tailor training to every role

Use role-specific paths designed for general users, engineers, and future AI researchers.

Build skills without slowing down

Learn in just 3 to 6 hours a week while staying focused on core responsibilities.

You're in good company

Customer stories

CodeSignal’s experiential learning platform fast-tracks skill mastery beyond what’s possible with traditional methods. It’s next-gen AI learning done right.

Josh Bersin

Josh Bersin Company

I’m proud to share my Certification from CodeSignal Learn! Diving deep into AI algorithms was challenging, yet incredibly rewarding. It wasn’t easy, but the more difficult the task, the more satisfying the accomplishment!

Oleksandr P.

I recently completed my first course on CodeSignal Learn, practicing Java syntax and working in their easy to use IDE. My favorite part of the course was working with Cosmo, the super cute corgi that answers any questions I might have as I’m learning!

Beatriz G.

Partnering with CodeSignal has helped us to manage a very high volume of interest from candidates in our process and quickly assess their technical acumen, without using a ton of engineering hours.

Nadia Abouzaid

Head of Diversity Talent Programs at Asana

By incorporating CodeSignal into our process and having a large number of folks opt into it, either passive candidates or applicants, we’re able to free up roughly 40 to 60% of our engineers’ time.

Michael Leggett

Tech Recruiter at Outreach

CodeSignal has been received very well by the product engineering team. We’ve tried multiple different solutions in this space, and [CodeSignal’s] interactivity, reliability, and language support has really helped us.

Tim Johnson

Engineering Manager at Greenhouse

Results our
customers have seen

Increase in skill mastery

Average retention rate