AI-Assisted Coding for Developers
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Learn to prompt AI coding assistants for debugging, documentation and tests, and to choose models and set guardrails for AI coding work.
Generating functions, catching bugs and improving documentation are jobs an AI coding assistant handles well only when it is prompted effectively. The lessons show how large language models fit into software work, how the context you supply changes their answers, and how to refine a prompt over several rounds. Later topics turn to tests, security and commit messages, then to choosing a model by cost and quality and placing guardrails around an AI workflow.
What You Will Achieve
- Write and iterate on prompts until an AI assistant’s code suggestions become usable changes
- Give a model the right context and role definition so its debugging help fits your code
- Have an AI assistant draft documentation, commit messages and tests, then check those tests for gaps
- Pick a model for a coding task by weighing cost against quality, and set guardrails that keep its output secure
Program Overview
AI-Assisted Coding and Prompt Basics
How language models support completion, debugging and documentation, and how prompts and context shape what they return.
- Matching AI models and workloads to tasks
- Prompts for decoding and changing functions
- Adding and choosing context
- Refining prompts through iteration
Prompting for Debugging, Documentation and Tests
Advanced prompts that guide a model through harder coding work, with role definitions, AI-written documentation and test generation.
- Debugging prompts and role definitions
- Code documentation and commit messages
- Test generation and gaps in AI-generated tests
- Prompts for secure code
Model Choice, Cost and Guardrails
Running AI coding assistants in real work by picking a tool, weighing reasoning models and keeping output safe and affordable.
- Choosing an AI tool and understanding its cost
- Trade-offs of reasoning models
- Security and ethical guardrails
- Balancing speed, cost and quality
Certificate Awarded (Paid)
Job Outlook
These skills apply wherever developers work with AI coding assistants, including writing and reviewing Python code, keeping documentation and tests current, and applying guardrails so AI models are used safely and ethically.
Good for: Python developers who use or plan to use an AI coding assistant and want more reliable, secure results from the prompts they write.
Keep in mind: It teaches prompting techniques meant to work across AI coding assistants rather than a guided tour of one product, so practice the prompts in the assistant you use.
Bottom line: A short course on prompting AI assistants for code that carries the topic on to test gaps, model cost and security guardrails.
Frequently Asked Questions
What does the course teach about context in prompts?
Early lessons show how adding context makes an AI model's responses smarter, how to choose the right context for a coding task, and how iterating on a prompt improves the code it returns.
What security topics come up in the lessons?
Security appears in two places. One lesson set covers prompts that steer a model toward secure AI-assisted code, and the final chapter adds security and ethical guardrails to an AI workflow and shows how to apply them.
How does the course handle the cost of AI assistants?
The last chapter looks at what AI assistants cost to run, the trade-offs of reasoning models, and strategies to control model cost while balancing performance, speed and the quality of the generated code.
Which developer tasks besides writing code does AI help with here?
The lessons apply AI to debugging, documenting code, writing commit messages and generating tests. They also show how to spot gaps in tests an AI model writes, so you can judge its output before relying on it.
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