Applied Prompt Engineering is a structured, practical course covering Prompting, Chain-of-thought, RAG. It emphasizes explainable methods, checked work and a final outcome that can be reviewed.
AI toolsTool stack
OpenAIClaude
Technology marks for Applied Prompt Engineering, sourced from the CC0-licensed Simple Icons project.
Use Prompting appropriately in a realistic, bounded task.
Use Chain-of-thought appropriately in a realistic, bounded task.
Use RAG appropriately in a realistic, bounded task.
Course outline and revision prompts
Module 1
Prompting: Foundations
Apply Prompting through capabilities, limitations, responsible use.
Lesson
Key terms
Revision question
Capabilities with Prompting
capabilities, Prompting, ai-tools
In Applied Prompt Engineering, which evidence best supports a capabilities result produced with Prompting?
Limitations with Chain-of-thought
limitations, Chain-of-thought, ai-tools
In Applied Prompt Engineering, which evidence best supports a limitations result produced with Chain-of-thought?
Responsible use with RAG
responsible use, RAG, ai-tools
In Applied Prompt Engineering, which evidence best supports a responsible use result produced with RAG?
Instructional figureProcess flow
Capabilities: from context to evidence
Capabilities connects bounded input to a reviewed output in Applied Prompt Engineering.
1Bounded input
Define the purpose, intended user and Prompting constraints.
frames
2Capabilities
Compare representative inputs and record where performance changes.
produces evidence for
3Reviewed output
Compare the observed result with a normal case, boundary case and stated limitation.
Takeaway: Capabilities is credible only when the result can be traced back to its purpose, inputs and constraints. Capabilities is the decision layer between the starting context and evidence that the result is fit for purpose.
Module 2
Chain-of-thought: Working methods
Apply Chain-of-thought through clear instructions, context design, verification.
Lesson
Key terms
Revision question
Clear instructions with Prompting
clear instructions, Prompting, ai-tools
In Applied Prompt Engineering, which evidence best supports a clear instructions result produced with Prompting?
Context design with Chain-of-thought
context design, Chain-of-thought, ai-tools
In Applied Prompt Engineering, which evidence best supports a context design result produced with Chain-of-thought?
Verification with RAG
verification, RAG, ai-tools
In Applied Prompt Engineering, which evidence best supports a verification result produced with RAG?
Instructional figureContinuous cycle
Clear Instructions: from context to evidence
Clear Instructions connects bounded input to a reviewed output in Applied Prompt Engineering.
1Bounded input
Define the purpose, intended user and Prompting constraints.
frames
2Clear Instructions
Rewrite a vague request as a bounded specification, then compare outputs.
produces evidence for
3Reviewed output
Compare the observed result with a normal case, boundary case and stated limitation.
Review the evidence, adjust the method, and repeat.
Takeaway: Clear Instructions is credible only when the result can be traced back to its purpose, inputs and constraints. Clear Instructions is the decision layer between the starting context and evidence that the result is fit for purpose.
Module 3
RAG: Applied workflows
Apply RAG through research, creation, automation.
Lesson
Key terms
Revision question
Research with Prompting
research, Prompting, ai-tools
In Applied Prompt Engineering, which evidence best supports a research result produced with Prompting?
Creation with Chain-of-thought
creation, Chain-of-thought, ai-tools
In Applied Prompt Engineering, which evidence best supports a creation result produced with Chain-of-thought?
Automation with RAG
automation, RAG, ai-tools
In Applied Prompt Engineering, which evidence best supports a automation result produced with RAG?
Instructional figureProcess flow
Research: from context to evidence
Research connects bounded input to a reviewed output in Applied Prompt Engineering.
1Bounded input
Define the purpose, intended user and Prompting constraints.
frames
2Research
Build a question matrix and inspect primary sources before synthesising.
produces evidence for
3Reviewed output
Compare the observed result with a normal case, boundary case and stated limitation.
Takeaway: Research is credible only when the result can be traced back to its purpose, inputs and constraints. Research is the decision layer between the starting context and evidence that the result is fit for purpose.
Module 4
Prompting: Quality and review
Apply Prompting through evaluation, privacy, repeatable practice.
Lesson
Key terms
Revision question
Evaluation with Prompting
evaluation, Prompting, ai-tools
In Applied Prompt Engineering, which evidence best supports a evaluation result produced with Prompting?
Privacy with Chain-of-thought
privacy, Chain-of-thought, ai-tools
In Applied Prompt Engineering, which evidence best supports a privacy result produced with Chain-of-thought?
Repeatable practice with RAG
repeatable practice, RAG, ai-tools
In Applied Prompt Engineering, which evidence best supports a repeatable practice result produced with RAG?
Instructional figureSide-by-side comparison
Evaluation: from context to evidence
Evaluation connects bounded input to a reviewed output in Applied Prompt Engineering.
1Bounded input
Define the purpose, intended user and Prompting constraints.
frames
2Evaluation
Build a test set with pass conditions, edge cases and failure categories.
produces evidence for
3Reviewed output
Compare the observed result with a normal case, boundary case and stated limitation.
Takeaway: Evaluation is credible only when the result can be traced back to its purpose, inputs and constraints. Evaluation is the decision layer between the starting context and evidence that the result is fit for purpose.
Course practical outcome
Produce a reviewable Applied Prompt Engineering project using Prompting, Chain-of-thought, RAG.
Expected output: A working Applied Prompt Engineering artefact plus an evidence-based self-review.
Tools: A suitable Prompting environment, A plain-text decision log, Test data or realistic sample material
Production steps
Define the intended user, outcome and constraints.
Create the smallest complete result using Prompting.
Test one normal case, one boundary case and one failure response.
Revise the work from the evidence and preserve before-and-after results.
Prepare a concise handover containing method, limitations and next step.
Success criteria
The output matches the stated outcome.
Inputs and decisions are reproducible.
Boundary and failure evidence is included.
Limitations and responsibility considerations are explicit.
Self-review
Can another learner repeat the method?
Did I test a difficult case?
Did I avoid unsupported claims?
Is the next action proportionate to the remaining risk?
Next step: Choose one weakness found during review and improve it before extending the Prompting scope.
Glossary
Prompting
A core concept or tool used in Applied Prompt Engineering; its exact meaning is established in the relevant lesson.
Chain-of-thought
A core concept or tool used in Applied Prompt Engineering; its exact meaning is established in the relevant lesson.
RAG
A core concept or tool used in Applied Prompt Engineering; its exact meaning is established in the relevant lesson.
This guide is generated from DigiLearn course material. Product versions, regulations and professional standards can change; consult the linked authoritative source before applying version-sensitive guidance.