Prompt Engineering
Develop the skills to build a tested prompt set for a defined task through a learning path centred on Prompt Engineering. Explore concepts, guided practice and an applied project.
Build practical Prompt Engineering skills.
Skills and knowledge developed in this course.
- Task specification
- Prompt structure
- Iteration and evaluation
- Operational safeguards
- Applied project: Build a tested prompt set for a defined task
- Prompt Engineering — validation and review
About this course
This course develops practical capability in Prompt Engineering. Its course-specific content covers Task specification, Prompt structure, Iteration and evaluation, Operational safeguards, followed by an applied project focused on build a tested prompt set for a defined task. The lessons include concrete configuration, design, testing or operational decisions appropriate to the subject.
Who this is for
Administrators, developers and practitioners who work with Prompt Engineering or plan to take on responsibilities in this area.
Before you start
Working knowledge of artificial intelligence & generative ai and practical familiarity with the named platform or subject. Review the course topics before enrolment to confirm the lab starting point.
Build a tested prompt set for a defined task
Deliver build a tested prompt set for a defined task. Use the module outcomes to state requirements, demonstrate a working result, test an expected failure or boundary case, and document assumptions and follow-up work.
Course content
Course-specific syllabus and practical exercises. Lab release, access arrangements and final delivery scope are confirmed before enrolment.
01Task specification
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- Instructions, examples and constraints
- Separate trusted instructions from input data
Practical exerciseApply task specification to the build a tested prompt set for a defined task and capture the validation evidence.
02Prompt structure
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- Few-shot examples and delimiters
- Output schemas and response validation
Practical exerciseApply prompt structure to the build a tested prompt set for a defined task and capture the validation evidence.
03Iteration and evaluation
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- Test datasets and scoring rubrics
- Compare prompt revisions and failure cases
Practical exerciseApply iteration and evaluation to the build a tested prompt set for a defined task and capture the validation evidence.
04Operational safeguards
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- Context budgeting and truncation
- Injection resistance and escalation rules
Practical exerciseApply operational safeguards to the build a tested prompt set for a defined task and capture the validation evidence.
05Applied project: Build a tested prompt set for a defined task
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- Define inputs, assumptions and acceptance criteria for: Build a tested prompt set for a defined task.
- Implement the project using task specification, prompt structure, iteration and evaluation, operational safeguards.
Practical exerciseBuild a tested prompt set for a defined task.
06Prompt Engineering — validation and review
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- Demonstrate the completed project against its acceptance criteria.
- Exercise an invalid-input or failure scenario and document the observed result.
Practical exercisepresent the project evidence, limitations and a repeatable handover.
Technical references
These primary vendor, project or standards sources inform this independent IT Purview outline. Reference pages checked 25 October 2026; current provider documentation and exam objectives take precedence when versions change.
From understanding to application.
Progress by learning milestone. The schedule is agreed around your starting level and learning goals.
- 01Learning milestone 1 In progress
Set the context
Task specification · Prompt structure
Establish the Prompt Engineering scope, baseline concepts and operating assumptions. - 02Learning milestone 2 In progress
Build the working skills
Iteration and evaluation · Operational safeguards
Apply the core Prompt Engineering techniques in the course lab and record the result. - 03Learning milestone 3 In progress
Validate and handle exceptions
Check expected behavior, investigate a failure case and capture evidence for build a tested prompt set for a defined task. - 04Learning milestone 4 In progress
Deliver the applied project
Complete build a tested prompt set for a defined task, explain key decisions and identify a practical next step.
Career outlook after this learning path
Typical roles this roadmap can support, with broad salary ranges by relevant work experience.
| Role | 0–3 years | 3–7 years | 7–12+ years |
|---|---|---|---|
| AI / ML Engineer | ₹10–17 LPA | ₹20–35 LPA | ₹38–62 LPA |
Ranges are indicative India-market compensation estimates in ₹ lakh per annum, not a salary promise. They reflect broad market guides and vary by location, employer, skills and role scope; the experience bands refer to relevant work experience, not time since training. Completing this course does not guarantee employment, a role, compensation or career progression. View market reference ↗