Curriculum Overview: Strategic Capabilities and Limitations of GenAI for Business

This curriculum provides a comprehensive framework for understanding how Generative AI (GenAI) can be leveraged to solve complex business problems while navigating the inherent risks and technical constraints of the technology. Grounded in the AWS Certified AI Practitioner (AIF-C01) domains, this course covers everything from value identification to performance evaluation.

---

Prerequisites

Before starting this curriculum, learners should have a foundational understanding of the following concepts:

• Cloud Computing Fundamentals: Basic knowledge of cloud service models (SaaS, PaaS, IaaS) and AWS core concepts.
• Basic AI/ML Terminology: Familiarity with terms like "model," "training," "inference," and "dataset."
• Data Literacy: Understanding the difference between structured and unstructured data.
• Business Logic: Ability to define standard KPIs (Key Performance Indicators) such as ROI, conversion rates, and operational efficiency.

---

Module Breakdown

---

Learning Objectives per Module

Module 1: Core GenAI & Business Value

• Define foundational concepts: Tokens, Embeddings, and Foundation Models (FMs).
• Calculate business value using metrics like Average Revenue Per User (ARPU) and Customer Lifetime Value (CLV).
• Differentiate between System 1 (Fast/Reactive) and System 2 (Reasoning/Agentic) AI thinking.

Module 2: Strategic Advantages & Use Cases

• Identify opportunities for Automation, Content Creation, and Summarization.
• Explain the benefits of Scalability and Personalization in user experience.
• Compare AI techniques: Regression vs. Classification vs. GenAI.

Module 3: Limitations, Risks, and Ethics

• Recognize and mitigate Hallucinations and Nondeterminism.
• Understand "Black Box" issues: Interpretability and Accountability.
• Identify security threats: Prompt Injection, Jailbreaking, and Poisoning.

Module 4: Implementation & AWS Ecosystem

• Map business needs to AWS services: Amazon Bedrock, Amazon Q, and SageMaker JumpStart.
• Assess cost trade-offs between RAG (Retrieval Augmented Generation) and Fine-Tuning.
• Describe the role of Agents in multi-step autonomous tasks.

Module 5: Evaluation & Model Selection

• Utilize automated metrics: ROUGE, BLEU, and BERTScore.
• Implement human evaluation frameworks including Net Promoter Score (NPS).
• Select models based on Latency, Modality, and Compliance requirements.

---

Visual Anchors

GenAI Business Decision Logic

The Trade-off Matrix: Customization Approaches

---

Success Metrics

To demonstrate mastery of this curriculum, the learner must be able to:

1. Architecture Design: Propose a GenAI architecture for a specific case study (e.g., Customer Support) using Amazon Bedrock Agents.
2. Risk Mitigation: Identify at least three potential risks (e.g., bias, cost, hallucination) in a proposed solution and provide mitigation strategies.
3. Cost-Benefit Analysis: Compare the Token-based pricing of a managed API vs. the Provisioned Throughput of a custom model.
4. Metric Selection: Choose the correct evaluation metric (e.g., BERTScore for semantic similarity vs. BLEU for translation accuracy).

---

Real-World Application

[!IMPORTANT] GenAI is not a "silver bullet." It is most effective when integrated into existing business workflows rather than replacing them entirely.

Industry Examples

• Finance: Using GenAI for fraud detection narratives and summarizing regulatory compliance documents, while maintaining human-in-the-loop for final approval.
• Healthcare: Generating patient summaries from clinician notes (Summarization), while strictly managing the risk of Hallucinations in medical dosages.
• Retail: Implementing Personalization through recommendation engines that use embeddings to find products semantically similar to a user's past purchases.

▶Click to view: When NOT to use GenAI

Avoid GenAI when:

1. Deterministic Logic is Required: If $X$ must always equal $Y$ based on a simple math formula.
2. High-Stakes Safety: Where a 0.1% inaccuracy (Hallucination) could lead to physical harm.
3. Vague Objectives: When the business cannot define what a "successful" output looks like.