LLM Evaluation and Observability Essentials
A reproducible evaluation report and trace set. Results are reproducible on the fixed dataset and include at least one failure case.
Register your interest. Dates and delivery are confirmed before enrolment opens.
What you will be able to do.
Build understanding, put it into practice and finish with work you can explain.
- Results are reproducible on the fixed dataset and include at least one failure case.
Is this for you?
Intermediate learners who can demonstrate the entry competencies listed below. Preparation suggestions are optional.
Before you begin
Write a small function, branch on input and explain an error path Explain method status headers and JSON; send one safe request
How you will show your progress
Results are reproducible on the fixed dataset and include at least one failure case.
From first principles to applied work.
Explain → Demonstration → Guided practice → Review
Planned outline. Lessons, trainer and schedule are confirmed before this course opens.
0190 live minutesDefine quality and safety criteria
10 min explanation · 15 min demonstration · 45 min practice · 20 min review
Define quality cost latency and access-control checks
02120 live minutesCreate representative evaluation cases
15 min explanation · 20 min demonstration · 60 min practice · 25 min review
Write a fixed evaluation set with answerable and unsafe cases
03180 live minutesRecord traces latency and usage
25 min explanation · 35 min demonstration · 90 min practice · 30 min review
Capture traces usage and outcome labels from mock or funded runs
0490 live minutesCompare versions and diagnose regressions
10 min explanation · 15 min demonstration · 45 min practice · 20 min review
Compare two versions and document a regression and a proposed fix
How you will practise and be assessed.
The planned assessment checks your result, your judgement and your ability to apply what you learned independently.
- 01
Build the promised result
Demonstrate the promised artifact
WHAT YOUR WORK SHOULD SHOWResults are reproducible on the fixed dataset and include at least one failure case.
- 02
Test a limit or failure
Compare two versions and document a regression and a proposed fix
WHAT YOUR WORK SHOULD SHOWThe documented failure/uncertainty is visible, and no prohibited action or unsupported success claim occurs.
- 03
Apply it independently
Independently explain and change one assumption in a reproducible evaluation report and trace set.
WHAT YOUR WORK SHOULD SHOWThe learner predicts and verifies the change; AI output is not accepted without understanding.
Where this course stands.
Lab assets and supported tool versions are confirmed before teaching
Explore the source reference- Course ID
- S010
- Content
- Course outline in review
- Trainer
- Instructor to be confirmed
- Enrolment
- Not open · requests welcome
- Publication
- Planned
- Catalogue date
- 2026-10-15
Want to learn this?
Share your interest. We’ll confirm availability, batch dates and the fee separately, so you can decide when the details are ready.
- Your course is already selected
- Just your name and email to get started
- No payment or booking commitment
Know what your batch includes.
Learning support, assessment requirements and any certificate terms are confirmed with the batch before enrolment. Include your goals in your course request so the proposed format can be matched to what you need.
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