GenAI Skills Academy with CodeSignal
Equip participants with the GenAI skills that matter. With role-aligned learning tracks, they develop the ability to use, integrate, and create with GenAI in real-world contexts.
Upskill participants with a flexible, role-based approach to GenAI learning
Through CodeSignal’s GenAI Skills Academy, participants gain hands-on, role-based training. Each learning track combines structured learning with practical tools to bring GenAI into everyday use.
Explore all three learning tracks
Each track is built around weekly, hands-on modules that focus on real tools and real outcomes. Participants learn just a few hours per week while staying focused on their day-to-day responsibilities.
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
Anyone
Prerequisites
None
Key Skills
GenAI capability awareness, GenAI proficiency and use, GenAI limitation awareness
Outcomes
Participants graduating from this track should have the skills and expertise to use AI effectively and responsibly in their day-to-day 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
Participants 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
Participants 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, without putting anything else on hold.
You're in good company
CodeSignal’s experiential learning platform fast-tracks skill mastery beyond what’s possible with traditional methods. It’s next-gen AI learning done right.
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!
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.
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.
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.
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