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Prompt Engineering
Prompt Engineering
Move from improvised prompts to structured, testable instruction systems with measurable output quality.
Level
Foundations
Duration
5 weeks
Lessons
20
Price
Price on request
What you'll learn
- Design system-prompt hierarchies, roles and constraints that hold under pressure.
- Use few-shot, decomposition and structured output schemas reliably.
- Build evaluation sets and score outputs objectively instead of by impression.
- Diagnose hallucination, drift and instruction conflict.
Course curriculum
01 — How instructions are interpreted
- Tokenisation and context
- Attention and ordering effects
- Temperature and sampling
02 — Instruction architecture
- System, developer and user layers
- Constraints and refusal design
- Structured output schemas
03 — Reliability
- Decomposition
- Self-checking patterns
- Failure taxonomies
04 — Evaluation
- Building a golden set
- Rubrics and scoring
- Regression testing prompts
Practical application
- · Rebuild an existing prompt as a versioned system and measure the quality delta.
- · Run a regression suite over a model change and report the impact.
Practical project: A versioned prompt system with an evaluation set and scoring rubric
Who is this for?
- · Professionals using AI daily
- · Content and operations teams
- · Product and technical roles
Requirements
- · Access to a modern AI assistant
- · No programming required
Meet your instructor
RAG & Knowledge Systems
Instructor to be announced
Chunking, embeddings, hybrid retrieval, re-ranking and grounding answers in verifiable sources.
The verified name, biography, professional experience and public profile for this chair are published once confirmed. INTELLCOREAI does not present unverified profiles as faculty.
Student reviews
Reviews are published from verified students only, after the first cohort completes the program. We do not publish testimonials we cannot attribute.
