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.
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 |
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 |
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 |
Real teams getting real results
Discover how Instacart, Nextdoor, and other innovators use CodeSignal to hire smarter, learn faster, and grow stronger.
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’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
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