Learning pathway
Introduction to Artificial Intelligence
What models can and cannot do, how to build with them, and how to tell whether yours works.
Potential prerequisites
- Basic Python, or willingness to learn it alongside
- Comfort reading technical documentation
Intended audience
Who this is for
- Developers and analysts adding applied AI to existing skills
- Domain experts who want to judge AI systems in their own field
- People exploring whether applied AI suits them before committing
Learning objectives
What you should be able to do
- 01Explain in plain language how modern models produce output
- 02Build a working application on top of a model API
- 03Design an evaluation set and measure failure modes, not just averages
- 04Ground a system in your own data using retrieval
- 05Identify where a model should not be used at all
Suggested modules
A sequence that builds on itself
Each module assumes the one before it. Skipping ahead usually costs more time than it saves.
- Module 01
How models actually behave
Training, inference, context, tokens and the reasons models produce confident errors.
- Conceptual fundamentals
- Module 02
Building with model APIs
Structuring prompts and context, handling errors, managing cost and latency.
- Python
- APIs
- Context design
- Module 03
Retrieval and grounding
Chunking, embeddings and retrieval so a system answers from sources rather than memory.
- Vector search
- Data pipelines
- Module 04
Evaluation
Building a test set, defining what good looks like, and tracking regressions. The module that separates a demo from a product.
- Evaluation design
- Error analysis
- Module 05
Safety, bias and limits
Where systems cause harm, how to scope responsibly, and what to escalate to a human.
- Risk judgement
- Documentation
- Module 06
Shipping and monitoring
Deployment, observability, and what to watch once real inputs arrive.
- Deployment
- Monitoring
Practical exercises and projects
What you build along the way
The projects are the point. They're what you can show someone afterwards.
- Project 01
Grounded question-answering tool
Build something that answers questions over a document set you care about, with citations back to the source.
- Project 02
Fifty-case evaluation
Write fifty test cases for your tool, score them, and publish an honest account of where it fails.
- Project 03
Scoping memo
Pick a task in your own field and argue in writing where a model should and should not be applied to it.
Suggested progression
Where it leads next
- Go deeper into ML fundamentals and model training
- Move toward AI infrastructure and platform work
- Apply it inside a domain where you can judge correctness
Related career directions
What this pathway serves
- Artificial IntelligenceBuild, evaluate and apply models — and judge honestly where they help and where they do not.
- Software DevelopmentDesign, write and maintain the systems people use — most of the job is reading and changing what exists.
- Data and AnalyticsTurn messy records into answers a team can act on — and explain the answer clearly.
This pathway is a conceptual curriculum, not an enrolled course. Career Mine does not provide accreditation or certification, does not host course delivery, and makes no claim about employment outcomes from following it.
Before you start
Check this is aimed at the right place.
A pathway is only useful if the destination still appeals once you know what the work involves.