🏫 Programming & Software Development

Prompt Engineering for Beginners

Master AI Orchestration - From Structural Frameworks to Agentic Prompting

Duration

12 Weeks

Weekly Hours

4 Hours

Course Incharge

Muzammil Bilwani

📋 Prerequisites

✓ Beginner to Intermediate

📖 Course Description

Master the art and science of prompt engineering. Learn the underlying architecture of LLMs, structural frameworks, and advanced tuning parameters to build professional-grade AI workflows.

What You Will Learn

Understand LLM architecture, tokens, and context windows

Apply structural frameworks like RTF and XML tagging

Implement Chain of Thought and Multi-Prompt workflows

Control AI output formats (JSON, CSV, Markdown)

Tune parameters like Temperature and Top-P

Build a professional business-in-a-box prompt library

Course Outline

The Engine Under the Hood (Technical Foundations)

→LLM Architecture for Beginners: What are Tokens? How "Context Windows" work (and why AI forgets)
→Probability vs. Logic: Understanding that AI predicts the next word (stochastic parrots)
→The Anatomy of a Technical Prompt: Role, Context, Task, Constraints, and Output Format
→Tooling: Comparing the "logic" of GPT-4o, Claude 3.5 Sonnet (the coder's favorite), and Llama 3
→Outcome: Students understand why a prompt fails based on token limits or logic gaps

Structural Frameworks (Architecture of a Prompt)

→The RTF (Role-Task-Format) Framework: Building high-precision instructions
→Delimiter Mastery: Using ###, ---, or """ to separate data from instructions (preventing prompt injection)
→Markdown for Prompts: Why AI loves headers (#), bullet points, and tables
→XML Tagging: Using <instruction> and <context> tags to give the AI clear boundaries
→Outcome: Students move from "paragraph prompts" to structured "architectural prompts"

Logic Stepping (Chain of Thought)

→Chain of Thought (CoT): Forcing the AI to "Think Step-by-Step" for complex math or logic
→Few-Shot Prompting: Providing 3–5 examples to "program" the AI's behavior without code
→Zero-Shot vs. One-Shot: When to give examples and when to let the AI lead
→The "System Prompt": Understanding the hidden instructions that govern AI behavior
→Outcome: Solving multi-step logic problems that usually make AI "hallucinate"

Data Input & Technical Formatting

→Feeding the Machine: How to paste 50-page PDFs or long data lists effectively
→Output Control: Forcing AI to output in specific formats: JSON, CSV, Markdown, or Mermaid.js (for diagrams)
→Data Cleaning: Using AI to transform messy text into structured tables
→Outcome: Students can turn a messy interview transcript into a structured JSON database

Advanced Parameters & Prompt Tuning

→The Control Knobs: Understanding Temperature (Creativity vs. Precision), Top-P, and Frequency Penalty
→Prompt Iteration: The "Debug" cycle—how to fix a prompt that is 90% correct
→Handling Hallucinations: Implementing "Check your work" loops within a prompt
→Outcome: Students can "tune" an AI to be either a creative poet or a rigid technical auditor

Prompt Chaining & Multi-Prompt Workflows

→The Modular Approach: Breaking a massive task into 3 small prompts (e.g., Summarize -> Analyze -> Draft)
→Sequential Logic: Using the output of Prompt A as the input for Prompt B
→Introduction to "Custom GPTs": Building a persistent AI tool with specialized knowledge instructions
→Outcome: Designing a multi-stage workflow that writes a full researched article in 3 steps

Multi-Modal & Specialized Prompting

→Image Engineering: Technical prompting for Midjourney/DALL-E (Aspect ratios, lighting, camera lens logic)
→Vision Prompts: Uploading an image or a UI sketch and asking the AI to explain it or turn it into code
→Agentic Prompting (Intro): Giving AI "Tools" and "Permissions" to act as an agent
→Outcome: Students create a technical UI design based on a hand-drawn sketch

The Professional Prompt Library (Capstone)

→The Project: Build a "Business-in-a-Box" Prompt Library
→Deliverable: A collection of 5 complex, structured prompts that solve a specific industry problem
→Evaluation: Testing prompts against edge cases (purposely trying to break them)

📊 Grading Criteria

Component	Percentage
Quizzes	20%
Class Participation / Attendance	15%
Projects	25%
Final Projects	40%
Total	100%

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