Prompt Engineering
First 2027 scholarship call
Prompt Engineering
Move from improvised prompts to structured, verifiable instruction systems with measurable quality.
Developed under the new AI Act model · Continuous updates
Edition 2026
200 hours of applied learning
8 academic credits
25 lessons + 6 workshops
Digital campus
Verifiable certificate
550 USD one payment · or 2 × 275 USD (today and after 30 days)
What you'll learn
- Explain how a model interprets instructions: tokens, context, attention, order and sampling.
- Design system-prompt hierarchies, roles and constraints that hold under pressure.
- Use few-shot examples, decomposition and structured output schemas reliably.
- Diagnose hallucination, drift, instruction conflict and prompt injection.
- Build evaluation sets and score outputs objectively, not by impression.
- Manage context, reasoning, cost and versions of a prompt system in production.
- Apply the IntellCore AI Act Method™: classify the risk of an AI use case in your field and document it under the AI Act 2026.
Program curriculum
01 — How instructions are interpreted
- What AI is and how it reached instruction engineering
- Tokenization and the context window
- Attention and order effects
- Temperature and sampling
- Anatomy of an instruction: from request to specification
02 — Instruction architecture
- System, developer and user layers
- Constraints and refusal design
- Few-shot examples: choose, order and avoid contamination
- Structured output schemas
03 — Reliability
- Decomposition and chaining
- Self-checking patterns
- Failure taxonomies: hallucination, drift and instruction conflict
- Prompt injection and untrusted content
04 — Evaluation
- Building a golden set
- Rubrics and scoring
- Judges: rules, people and models
- Prompt regression testing and model changes
05 — Context, reasoning and cost
- Context engineering: what goes into the window
- Reference documents and grounded answers
- Reasoning models and effort control
- Prompt caching, latency and token budget
06 — Prompt systems in production
- Version management and change log
- System governance: owner, data and transparency
- Production monitoring and drift
- Final project: a versioned prompt system with an evaluation set and rubric
07 — IntellCore AI Act Method™ applied to your field
- The EU AI Act 2026: risk levels, prohibited practices and obligations
- Transparency, human oversight and documentation in your professional field
- Classifying and auditing a real AI use case step by step
- Your compliance file with the IntellCore AI Act Method™: from system records to the review plan
Practical application
- · Final project: a versioned prompt system with an evaluation set and scoring rubric that rebuilds an existing prompt, measures the improvement against its baseline, includes a regression report for a model change and is defended before a decision-maker.
Practical project: Final project: a versioned prompt system with an evaluation set and scoring rubric that rebuilds an existing prompt, measures the improvement against its baseline, includes a regression report for a model change and is defended before a decision-maker.
Who is this for?
- · Professionals who use AI daily
- · Content and operations teams
- · Product and technical profiles
Requirements
- · Access to a modern AI assistant
- · No coding required
Meet your instructor
RAG & Knowledge Systems
Chair, RAG & Knowledge Systems
Chunking, embeddings, hybrid retrieval, re-ranking and grounding answers in verifiable sources.
Teaches: Prompt Engineering · Generative AI
Edition 2026
What each week includes
Every week of the program follows the same structure.
- 01
Explainer video per lesson
- 02
Guided reading (6 h)
- 03
IntellCore Labs · ~90 min workshop with data
- 04
Verified real case
- 05
IntellCore Podcast
- 06
IntellCore Radar
- 07
Self-assessment
- 08
Practice and exam (70%, 3 attempts)
- 09
Extended practice (5 h)
- 10
IntellCore AI Act Method™ module applied to your field (30 h)
- 11
Applied final project (30 h)
- 12
6 modules × 23 h + IntellCore AI Act Method™ module 30 h + final assessment 2 h + final project 30 h = 200 h
- 13
Downloadable PDF summary
Verifiable certificate
200 hours · 8 academic credits
Unique verifiable code at intellcore.ai/verify
Student reviews
Reviews are published from verified students only, after the first cohort completes the program. We do not publish testimonials we cannot attribute.
