Curriculum Overview: Prompt Engineering Techniques

Welcome to the curriculum overview for Prompt Engineering Techniques. As artificial intelligence and large language models (LLMs) become central to modern business applications, the ability to effectively guide these models through well-crafted prompts is no longer just an art—it is a critical engineering discipline.

This curriculum provides a structured path from the foundational anatomy of a prompt to advanced reasoning techniques like Chain-of-Thought (CoT), equipping you to build robust, secure, and highly accurate AI applications.

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Prerequisites

Before diving into this curriculum, learners should have a solid foundation in the following areas:

• Foundation Models (FMs) & LLMs: A basic understanding of what Transformer-based models are and how they generate text based on probabilities ($P(token|context)$).
• Basic AI/ML Terminology: Familiarity with terms like inference, training, fine-tuning, and hallucinations.
• Cloud Concepts: General knowledge of cloud infrastructure (e.g., AWS public cloud models) and API-based interactions with managed AI services (like Amazon Bedrock).

[!IMPORTANT] If you are completely new to Generative AI, we recommend completing a "Fundamentals of Generative AI" primer before starting this curriculum. You must understand why models predict the next token to fully grasp how prompt engineering influences that prediction.

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Module Breakdown

The curriculum is structured sequentially, beginning with basic prompt construction and advancing toward sophisticated reasoning techniques and security considerations.

Module 1: The Anatomy of a Prompt

• Topic 1.1: Deconstructing Prompts (Instructions, Context, Input Data, Output Indicators)
• Topic 1.2: Best Practices for Clarity and Concision
• Topic 1.3: The Role of Model-Specific Syntax (e.g., System vs. User prompts)

Module 2: Core Prompting Techniques

• Topic 2.1: Zero-Shot Prompting (Relying on pre-trained weights)
• Topic 2.2: Single-Shot & Few-Shot Prompting (Providing in-context examples)
• Topic 2.3: Dynamic Few-Shot Prompting (Using vector databases for relevant example retrieval)

Module 3: Advanced Reasoning & Context

• Topic 3.1: Chain-of-Thought (CoT) & Step-by-Step Prompting
• Topic 3.2: Role-Playing for Domain-Specific Responses
• Topic 3.3: Retrieval-Augmented Generation (RAG) vs. In-Context Learning

Module 4: Prompt Templates & Automation

• Topic 4.1: Designing Prompt Templates
• Topic 4.2: Constraining Outputs (Length, Style, Format)
• Topic 4.3: Prompt Routing based on Input Classification

Module 5: Security & Governance

• Topic 5.1: Identifying Prompt Injection & Jailbreaking
• Topic 5.2: Mitigating Risks (Exposure, Model Poisoning)
• Topic 5.3: Ethical Considerations & Guardrails

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Learning Objectives per Module

Upon successful completion of the respective modules, learners will be able to:

• Module 1:
  • Identify and construct the four core components of a successful prompt.
  • Avoid leading questions and utilize analogies to improve model comprehension.
• Module 2:
  • Distinguish between zero-shot and few-shot scenarios.
  • Implement dynamic few-shot prompting to constrain and guide an LLM to a specific output format.
• Module 3:
  • Apply Chain-of-Thought (CoT) techniques to force the model to explicitly state its reasoning steps, reducing logic errors in complex tasks.
• Module 4:
  • Develop reusable prompt templates that programmatically insert context and data variables.
• Module 5:
  • Define potential risks of prompt engineering, including malicious hijacking and exposure.
  • Implement defensive prompt structures to neutralize prompt injection attacks.

Visualizing the Anatomy of a Prompt

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Success Metrics

How will you know you have mastered the curriculum? Mastery is evaluated through both objective automated metrics and subjective quality checks:

[!TIP] Experimentation is key. Prompt engineering is often described as more art than science. Success is highly correlated with iterative tweaking. A good prompt engineer measures success by how systematically they test and refine prompts rather than getting it perfect on the first try.

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Real-World Application

Prompt engineering is not just a theoretical exercise; it is the primary interface for building enterprise AI applications.

1. Customer Service Automation

Without prompt templates, an LLM might answer a customer query conversationally but fail to trigger internal systems. Using Few-Shot Prompting, you can instruct the model to act as a routing agent:

• Input: "The app crashes whenever I try to open it."
• Constrained Output: Technical Support

2. Complex Decision Support

For legal or financial document analysis, a zero-shot prompt might result in skipped details or hallucinations. By applying Chain-of-Thought (CoT), you can force the model to outline its reasoning:

• Prompt: "Explain your reasoning step by step before concluding whether this contract allows for termination for convenience."
• Result: The model breaks down the logic, significantly reducing the chance of a hallucinated "Yes" or "No."

3. Mitigating AI Security Risks

Enterprises deploying public-facing AI bots risk "jailbreaking" (where a user tricks the AI into breaking its own rules). Understanding prompt injection allows engineers to design robust meta-prompts that wrap user inputs in strict operational boundaries, protecting company data and brand reputation.

By the end of this curriculum, you will transition from a casual AI user to an engineer capable of designing scalable, resilient, and business-ready generative AI solutions.