Prompt Engineering
Master prompt design, context management, few-shot techniques, task decomposition, and systematic evaluation to extract maximum performance from AI models.
Course Overview
The Prompt Engineering program is an intensive 1-month professional workshop focused on turning AI users into master prompt engineers. You will learn the mechanics behind transformer context windows, develop structured prompting frameworks, apply few-shot and chain-of-thought methodologies across multiple domains, and implement prompt evaluation and versioning systems.
General computer literacy and experience interacting with conversational AI interfaces.
Learning Objectives
- Understand LLM token mechanics, attention heads, context limits, and common failure modes.
- Structure reliable prompts utilizing role specifications, contextual framing, and strict delimiters.
- Apply advanced prompting techniques: Few-Shot, Chain-of-Thought (CoT), and ReAct patterns.
- Design custom prompt libraries for research, code generation, copywriting, and analytics.
- Evaluate prompt reliability across test cases, detect hallucinations, and maintain prompt versioning.
Tools & Technologies Mastered
Hands-on ToolingModule-by-Module Syllabus
Module 1:AI Fundamentals & Output Failure Modes
Week 1- How LLMs read text: tokens, probability distributions, temperature, and top-p parameters
- Context windows, needle-in-a-haystack limits, context rot, and memory constraints
- Common AI failure modes: hallucinations, sycophancy, instruction drift, and formatting breaks
Module Outcome: Diagnose and systematically document 5 common failure modes across major foundation models.
Module 2:Core Prompt Architecture & Constraints
Week 2- The Anatomy of an Enterprise Prompt: Role, Objective, Context, Constraints, and Format
- Delimiter design (XML tags, Markdown fences, JSON blocks) to isolate untrusted input
- Writing explicit negative constraints and preventing common unwanted outputs
Module Outcome: Build a modular system prompt that reliably adheres to strict formatting constraints.
Module 3:Advanced Prompting Patterns & Decomposition
Week 3- Few-shot prompting: selecting, formatting, and balancing positive and edge-case examples
- Chain-of-thought (CoT), Step-Back prompting, and Self-Consistency sampling
- Task decomposition: breaking complex multi-stage tasks into sequential prompt chains
Module Outcome: Implement a 3-stage chain-of-thought prompt pipeline solving complex analytical reasoning.
Module 4:Domain-Specific Prompt Applications
Week 4 (Part 1)- Prompts for research and synthesis: deep information extraction and structured briefings
- Prompts for software engineering: code translation, test generation, and bug fixing
- Prompts for marketing, education, legal document review, and business analysis
Module Outcome: Develop domain-specific prompt suites tailored for business analysis and coding.
Module 5:Evaluation, Verification & Prompt Libraries
Week 4 (Part 2)- Quantitative prompt evaluation: establishing test sets, scoring rubrics, and regression tests
- Factual verification strategies, ground truth benchmarking, and prompt versioning
- Final assignment: Reusable prompt library and documented evaluation test suite
Module Outcome: Deliver a version-controlled prompt library with documented test cases and accuracy scores.
Enterprise Reusable Prompt Library & Documented Evaluation Benchmark
A comprehensive, version-controlled library of 25+ production-tested prompt templates across business domains, accompanied by automated test cases, evaluation scoring rubrics, and failure-mode documentation.
Learning Outcomes & Career Pathways
Produce deterministic, high-accuracy outputs from foundation AI models.
Isolate and structure complex operational instructions using modern prompt delimiters.
Evaluate and benchmark prompt performance across diverse model versions.
Package organizational knowledge into reusable prompt assets for entire teams.
Frequently Asked Questions
Course FAQsIs prompt engineering still relevant as AI models improve?
Yes. As models become more capable, prompt engineering shifts toward system architecture, deterministic output enforcement, context management, test evaluation, and workflow chaining.
Will I learn prompt techniques for multiple models?
Yes. The course covers prompt architectures tailored for OpenAI GPT-4o, Anthropic Claude 3.5 Sonnet, and Google Gemini.
Ready to Master Prompt Engineering?
Duration: 1 month. Live mentorship, practical projects, and a verifiable completion certificate.