Becoming an LLM Application Engineer
Learn what LLM application engineers do and build the skills and evidence to move into the role
A taste of a lesson
I've built a chatbot over my notes using a tutorial. How do I make it portfolio worthy?
Add the parts tutorials skip. First, an evaluation set: 30 to 50 questions with expected answers, including ones the documents can't answer. Measure accuracy and whether citations actually support answers. Then try one change, such as different chunking or adding keyword search, and show the before and after numbers. Record cost and latency per query. Finally, write up what failed and why. That shows engineering judgement. What kinds of questions does your current bot get wrong?
Written by the teacher as an example. In your lesson the tutor answers your own questions, and like any AI it can be wrong.
What you will be able to do
- Describe the day to day work and core skills of LLM application engineers
- Assess your skill gaps against the role's requirements
- Plan learning around durable skills like evaluation and retrieval
- Design a portfolio project with an evaluation set and documented trade offs
Lesson plan
- 1 The role in practice Understand what LLM application engineers build and how their time is spent. Start
- 2 Skills gap analysis Compare your current skills with the role's core requirements. Start
- 3 Durable foundations Focus learning on skills that outlast specific tools and models. Start
- 4 Evaluation as your differentiator Learn to build evaluation sets and measure LLM application quality. Start
- 5 Safety and reliability Understand prompt injection, data handling and failure modes. Start
- 6 Portfolio project plan Plan one substantial project that shows engineering judgement. Start
Try asking
About this tutor
For software developers and technical people interested in roles building products on top of large language models, often called AI engineers or LLM engineers. We cover what the role involves day to day: integrating model APIs, prompt and context design, retrieval, tool use, structured outputs, evaluation, cost and latency management, safety and guardrails. You assess your current skills, plan the learning that matters most, and design a portfolio project that demonstrates engineering judgement, especially evaluation. Honest about how quickly this area changes and that roles vary widely.
Reviews
4.7
3 ratingsSample
- Felix G.Sample
Adding an evaluation set to my RAG project was the turning point. Interviewers asked more about my eval than the app itself.
- Priyanka N.Sample
Clear map of the role and honest about how fast things change. The prompt injection lesson was eye opening.
- Andre L.Sample
As a backend developer this showed me how much of my existing skill transfers. Focused, practical and no hype.
About the teacher
Machine learning engineer who runs portfolio reviews and mock interviews for technical AI roles
9 tutors 243 lessons taught Sample
I work as a machine learning engineer and have spent a lot of my spare time reviewing portfolios and running mock interviews for people trying to get into ML, data and AI product roles. I have sat on both sides of the interview table, so I know what interviewers listen for and how often strong people undersell themselves. I teach...
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