Learn AI engineering.
A starting guide to building applications with models, working with data and checking whether the result is useful.
AI engineering covers several kinds of work, from integrating a model into an application to training and evaluating specialized systems. Different roles need different depth.
Clear prompts are useful, and they are one part of the work. AI applications also need software engineering. Inputs, permissions, errors, data quality and evaluation all affect the result.
Use the topics below to identify foundations and choose a practical project to learn from.
01 · FOUNDATIONS
Topics to build on
Start with enough programming and data knowledge to understand a small system end to end. Deepen the mathematics and infrastructure as your projects require.
1. Programming and application basics
Practise Python or another suitable language, API requests, asynchronous work, tests and error handling. Learn to protect credentials and understand what data leaves your application.
2. Data and retrieval
Learn to clean and organize source material, preserve its meaning and retrieve useful passages. Vector databases are one option; keyword search and other approaches can also be appropriate.
3. Retrieval and tool use
Explore retrieval-augmented generation (RAG) : supplying relevant external information to a model. Retrieval does not guarantee a correct answer, so check sources, coverage and how the response uses them.
4. Evaluation and reliability
Combine ordinary software tests with examples that assess model behavior. Examine wrong answers, latency, cost and failure recovery. Fine-tuning and model-based judging are options to evaluate, not requirements for every project.
02 · PRACTICE
Avoid skipping the checks
A successful demo is a useful start. Test how the system behaves with missing information, unusual inputs and unavailable services.
Look beyond the prompt
If an answer is poor, inspect the data, retrieval, tool results and instructions as well as the model. A clear workflow and a well-tested tool can matter as much as wording.
Build the mathematics you need
Probability, statistics and linear algebra help explain how many models and retrieval methods work. Learn them in manageable steps and connect each idea to an example.
03 · PRACTICAL WORK
A project to learn from
Choose a small collection of documents you have permission to use. Build a question-answering example that can point to the relevant material and say when it cannot answer.
Check data quality, latency and cost.
Example task: Help someone find an answer in a small set of product instructions, and make it clear when the instructions do not contain the answer.
Start with reliable extraction and a simple retrieval approach. Then test source relevance, missing answers, permissions and clear failure states before expanding the system.
Keep the project small enough that you can inspect the evidence and understand why an answer succeeded or failed.
Mentrast can help you build a path through programming, data, retrieval and evaluation. Work through explanations and exercises, then bring an external project back for discussion and feedback. Use the review to choose what to test and improve in your project.
Start learning