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Certificate in AI Software Development

Develop intelligent software applications using AI-assisted development tools, modern programming practices, and generative AI technologies.

Last updated 07/2026EnglishIntermediate

About this programme

Develop intelligent software applications using AI-assisted development tools, modern programming practices, and generative AI technologies.

Delivered as 60 live two-hour sessions across 8 modules, with a graded build at the end of every module and a mentor-reviewed capstone to finish.

Course outcomes

What you will be able to do on completion

This programme is built around capability, not coverage. By the end you will be able to:

01Work fluently with AI coding assistants without losing control of your codebase
02Design and ship application features powered by generative models
03Call model APIs with streaming, tool use and structured output
04Ground model responses in your own data through retrieval
05Write tests and evaluations for behaviour that is not deterministic
06Handle prompt injection, secrets and untrusted input safely
07Control token cost and latency in a production application
08Deploy, monitor and iterate on an AI feature
09Deliver a full AI-powered application reviewed by a mentor

Course content

8 modules · 60 sessions · 120 hours

Sessions run two hours each, live with an instructor. Each module closes with a graded build.

1.1 How AI is changing the developer workflowPreview2 hrs

1.2 Environment, tooling and version controlPreview2 hrs

1.3 Clean code and refactoring fundamentals2 hrs

1.4 Working effectively in a codebase you did not write2 hrs

1.5 Debugging strategy2 hrs

1.6 Reading and writing technical specifications2 hrs

1.7 Code review practice2 hrs

1.8 Setting up your AI development workspace2 hrs

2.1 How coding assistants actually work2 hrs

2.2 Prompting for code generation2 hrs

2.3 Scaffolding a feature with AI support2 hrs

2.4 Reviewing and correcting generated code2 hrs

2.5 Refactoring legacy code with AI help2 hrs

2.6 Generating and maintaining tests2 hrs

2.7 Documentation and commit messages2 hrs

2.8 Where assistants reliably fail2 hrs

2.9 Team conventions for AI-assisted work2 hrs

2.10 Workshop: shipping a feature end to end2 hrs

3.1 Calling model APIs from your application2 hrs

3.2 Sampling parameters and output control2 hrs

3.3 Streaming responses to a user interface2 hrs

3.4 Function calling and tool definitions2 hrs

3.5 Structured output and schema validation2 hrs

3.6 Conversation state and context windows2 hrs

3.7 Errors, retries and graceful degradation2 hrs

3.8 Build: a chat-enabled application2 hrs

4.1 Choosing the right AI feature to build2 hrs

4.2 Designing the interaction, not just the prompt2 hrs

4.3 Summarisation and extraction features2 hrs

4.4 Classification and routing features2 hrs

4.5 Assisted authoring and rewriting2 hrs

4.6 Search and recommendation with embeddings2 hrs

4.7 Multi-step assistant flows2 hrs

4.8 Fallbacks when the model is unavailable2 hrs

4.9 Accessibility and UX for AI features2 hrs

4.10 Project: an AI feature in a real app2 hrs

5.1 Why grounding matters2 hrs

5.2 Ingesting and cleaning source data2 hrs

5.3 Chunking and embedding strategy2 hrs

5.4 Vector stores and indexing2 hrs

5.5 Retrieval quality and re-ranking2 hrs

5.6 Citing sources in responses2 hrs

5.7 Caching and reuse2 hrs

5.8 Handling private and sensitive data2 hrs

5.9 Evaluating retrieval end to end2 hrs

5.10 Project: a grounded knowledge assistant2 hrs

6.1 Testing non-deterministic behaviour2 hrs

6.2 Building an evaluation harness2 hrs

6.3 Regression testing prompts and chains2 hrs

6.4 Prompt injection and untrusted input2 hrs

6.5 Secrets, keys and data leakage2 hrs

6.6 Dependency and supply-chain hygiene2 hrs

7.1 Packaging and configuration2 hrs

7.2 CI/CD for AI-enabled applications2 hrs

7.3 Observability and tracing2 hrs

7.4 Latency and cost at scale2 hrs

7.5 Rollout, flags and safe releases2 hrs

7.6 Incident response for AI features2 hrs

8.1 Capstone build and mentor review2 hrs

8.2 Portfolio, resume and interview preparation2 hrs

Requirements

A laptop with internet access
Willingness to work through hands-on builds each module
No prior AI background required — the first module starts from fundamentals
Around four hours a week outside sessions for project work

Description

Develop intelligent software applications using AI-assisted development tools, modern programming practices, and generative AI technologies.
60 live sessions across 8 modules, 120 hours in total.
Every module closes with a graded build reviewed by a mentor.
Finish with a verifiable certificate recognised by our hiring partners.

Instructor

Dr. Neha Rao

Lead AI Instructor · TEVVO Academy

12,480 reviews41,200 students9 courses

Neha has spent a decade building machine learning systems in production, most recently leading an applied AI team. She teaches the way she works — starting from a real problem, then building the smallest thing that solves it.

Student feedback

4.9Course rating

78%

14%

Featured reviews

Ananya S.

Structured, practical and paced properly. The graded build at the end of each module is what made it stick.

Karthik R.

The mentor reviews were the most valuable part — real feedback on real work, not just a quiz score.

Meera J.

Genuinely current material and a sensible schedule alongside a full-time job.