Free text-first course
LLMOps for Reliable AI Applications
A reliability report for an LLM app with eval cases, tests, traces, cost/latency notes, risk register, and release recommendation.
What you will be able to do
A reliability report for an LLM app with eval cases, tests, traces, cost/latency notes, risk register, and release recommendation.
Use a repeatable workflow instead of disconnected tips.
Check assumptions, evidence, output quality, and limitations before using results.
Create a capstone artifact that can be inspected, improved, and reviewed.
What you need before starting
Learners who have built at least one LLM app and want to evaluate, monitor, debug, and improve it with evidence.
Generative AI Application Development with Python, Introduction to RAG, and Introduction to Agentic AI are recommended.
Static lessons, activities, knowledge checks, learner templates, and a downloadable text-first pack.
12-module course sequence
The sequence follows the approved detailed syllabus and is implemented as a complete text-first shell.
Module 01
What Reliability Means for LLM Applications
- Learn how What Reliability Means for LLM Applications fits into reliable LLM application operations instead of treating it as an isolated topic.
- Practice by create or inspect a reliability check using the module idea, then compare the result with the module review checklist.
- Produce a reliability note with failure mode, eval case, trace/log evidence, and release decision before moving ahead in LLMOps for Reliable AI Applications.
Module 02
Quality Dimensions: Correctness, Grounding, Format, Safety, Latency, and Cost
- Learn how Quality Dimensions: Correctness, Grounding, Format, Safety, Latency, and Cost fits into reliable LLM application operations instead of treating it as an isolated topic.
- Practice by create or inspect a reliability check using the module idea, then compare the result with the module review checklist.
- Produce a reliability note with failure mode, eval case, trace/log evidence, and release decision before moving ahead in LLMOps for Reliable AI Applications.
Module 03
Eval Datasets and Test-Case Design
- Learn how Eval Datasets and Test-Case Design fits into reliable LLM application operations instead of treating it as an isolated topic.
- Practice by create or inspect a reliability check using the module idea, then compare the result with the module review checklist.
- Produce a reliability note with failure mode, eval case, trace/log evidence, and release decision before moving ahead in LLMOps for Reliable AI Applications.
Module 04
Unit Tests for Prompts, Schemas, and Tools
- Learn how Unit Tests for Prompts, Schemas, and Tools fits into reliable LLM application operations instead of treating it as an isolated topic.
- Practice by create or inspect a reliability check using the module idea, then compare the result with the module review checklist.
- Produce a reliability note with failure mode, eval case, trace/log evidence, and release decision before moving ahead in LLMOps for Reliable AI Applications.
Module 05
RAG Evaluation: Retrieval, Citation, and Answer Quality
- Learn how RAG Evaluation: Retrieval, Citation, and Answer Quality fits into reliable LLM application operations instead of treating it as an isolated topic.
- Practice by create or inspect a reliability check using the module idea, then compare the result with the module review checklist.
- Produce a reliability note with failure mode, eval case, trace/log evidence, and release decision before moving ahead in LLMOps for Reliable AI Applications.
Module 06
Agent Evaluation: Tool Trajectory, Approvals, and Stopping
- Learn how Agent Evaluation: Tool Trajectory, Approvals, and Stopping fits into reliable LLM application operations instead of treating it as an isolated topic.
- Practice by create or inspect a reliability check using the module idea, then compare the result with the module review checklist.
- Produce a reliability note with failure mode, eval case, trace/log evidence, and release decision before moving ahead in LLMOps for Reliable AI Applications.
Module 07
Observability: Traces, Metrics, and Logs
- Learn how Observability: Traces, Metrics, and Logs fits into reliable LLM application operations instead of treating it as an isolated topic.
- Practice by create or inspect a reliability check using the module idea, then compare the result with the module review checklist.
- Produce a reliability note with failure mode, eval case, trace/log evidence, and release decision before moving ahead in LLMOps for Reliable AI Applications.
Module 08
Cost, Latency, Caching, and Rate Limits
- Learn how Cost, Latency, Caching, and Rate Limits fits into reliable LLM application operations instead of treating it as an isolated topic.
- Practice by create or inspect a reliability check using the module idea, then compare the result with the module review checklist.
- Produce a reliability note with failure mode, eval case, trace/log evidence, and release decision before moving ahead in LLMOps for Reliable AI Applications.
Module 09
Red-Team and Misuse Cases
- Learn how Red-Team and Misuse Cases fits into reliable LLM application operations instead of treating it as an isolated topic.
- Practice by create or inspect a reliability check using the module idea, then compare the result with the module review checklist.
- Produce a reliability note with failure mode, eval case, trace/log evidence, and release decision before moving ahead in LLMOps for Reliable AI Applications.
Module 10
Release Gates and Regression Testing
- Learn how Release Gates and Regression Testing fits into reliable LLM application operations instead of treating it as an isolated topic.
- Practice by create or inspect a reliability check using the module idea, then compare the result with the module review checklist.
- Produce a reliability note with failure mode, eval case, trace/log evidence, and release decision before moving ahead in LLMOps for Reliable AI Applications.
Module 11
Incident Review and Continuous Improvement
- Learn how Incident Review and Continuous Improvement fits into reliable LLM application operations instead of treating it as an isolated topic.
- Practice by create or inspect a reliability check using the module idea, then compare the result with the module review checklist.
- Produce a reliability note with failure mode, eval case, trace/log evidence, and release decision before moving ahead in LLMOps for Reliable AI Applications.
Module 12
Capstone: Reliability Report for an LLM App
- Learn how Capstone: Reliability Report for an LLM App fits into reliable LLM application operations instead of treating it as an isolated topic.
- Practice by create or inspect a reliability check using the module idea, then compare the result with the module review checklist.
- Produce a reliability note with failure mode, eval case, trace/log evidence, and release decision before moving ahead in LLMOps for Reliable AI Applications.
Course status: outline published, lessons in development
The full syllabus, module structure, activities, and download pack are published and free to use now. Detailed lesson writing is still in progress, so lesson pages currently give the shape of each topic rather than the final teaching depth. Per-module assessments, worked examples, instructional diagrams, and external specialist review are planned next.