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CLF-C02 · FoundationalCloud Practitioner - Foundational
DVA-C02 · AssociateDeveloper - Associate
SAA-C03 · AssociateSolutions Architect - Associate
SOA-C02 · AssociateCloudOps Engineer - Associate
SAP-C02 · ProfessionalSolutions Architect - Professional
DOP-C02 · ProfessionalDevOps Engineer - Professional
SCS-C03 · SpecialtySecurity - Specialty
MLA-C01 · AssociateMachine Learning Engineer - Associate
AIF-C01 · FoundationalAI Practitioner - Foundational
  • Week 1
    • 1.The Relationship and Differences Between AI, Machine Learning, and Deep Learning
    • 2.Learning Types: Supervised, Unsupervised, and Reinforcement Learning
    • 3.Problems Where ML Fits vs. Problems Where It Doesn't
    • 4.Key Terms: Model, Training, Inference, Feature, Label, Overfitting
    • 5.Week 1 Wrap-Up: AI/ML Fundamentals Review
  • Week 2
    • 1.ML Lifecycle Overview: One Complete Cycle from Data to Operations
    • 2.Data: Structured vs Unstructured, Data Splitting, and the Power of Quality
    • 3.Model Evaluation Basics: Accuracy, Precision, Recall, and Overfitting/Underfitting
    • 4.The Human Role in ML Development: Labeling, Feedback, and Iterative Improvement
    • 5.Week 2 Summary: ML Lifecycle and Data at a Glance
  • Week 3
    • 1.What Is Generative AI: Difference from Traditional ML, and Foundation Models
    • 2.How LLMs Work: Tokens, Embeddings, Context Window, and Inference
    • 3.Prompt Engineering Basics: Good Prompts, Zero/Few-shot, and Limitations
    • 4.Limitations and Risks of Generative AI: Hallucinations, Bias, Non-determinism, and Appropriate Use Cases
    • 5.Week 3 Comprehensive Review: Wrapping Up Generative AI Fundamentals at a Glance
  • Week 4
    • 1.Amazon Bedrock: Fully Managed Service for Renting Foundation Models
    • 2.Amazon SageMaker: A Platform for Directly Training and Deploying ML Models
    • 3.AWS AI Services (1): Managed APIs Handling Images, Documents, Text, and Speech
    • 4.AWS AI Services (2) + Amazon Q: Chatbots, Search, Recommendations, Forecasting, and Generative Assistant
    • 5.Week 4 Comprehensive Review: Complete Map of AWS AI/ML Services
  • Week 5
    • 1.Principles of Responsible AI: Fairness, Bias, Transparency, Explainability, Robustness, Privacy
    • 2.AWS's Responsible AI Tools: SageMaker Clarify, Model Monitor, Bedrock Guardrails, AI Service Cards
    • 3.AI Security: Least Privilege IAM, Data Encryption, PII Protection, PrivateLink, Shared Responsibility Model
    • 4.Data Governance and Compliance: Data Origin·Quality, Model Governance, Audit·Logging, Legal and Ethical Considerations for Generative AI
    • 5.Week 5 Comprehensive Review: Binding Responsible AI·Security·Governance into One
  • Week 6
    • 1.Domain Review 1: AI/ML Fundamentals + Generative AI Fundamentals: Critical Summary
    • 2.Domain Review 2: Foundation Model Applications (AWS AI Services) Critical Summary
    • 3.Domain Review 3: Responsible AI + Security·Governance Critical Summary
    • 4.Full Mock Exam Pace: Five Domains Comprehensive Questions
    • 5.D-Day Wrap-Up: Exam Structure, Keyword → Service Translation Table, Frequently-Missed Traps
DEA-C01 · AssociateData Engineer - Associate
MLS-C01 · SpecialtyMachine Learning - Specialty
← AIF-C01/Week 3/Day 3
AIF-C01· AssociateWeek 3 · Day 3~20 min read

Day 3 - Prompt Engineering Basics: Good Prompts, Zero/Few-shot, and Limitations

The quality of answers varies greatly depending on "how you ask," even with the same model and same question. "Write something" and "Explain photosynthesis in a way a 4th grader can understand, in exactly 5 sentences, using friendly language" produce completely different results. This "skill of asking the model well" is called prompt engineering.

With generative AI, you can get a substantial portion of desired results without retraining the model—just by writing good prompts. Compared to training and fine-tuning, which are expensive and slow, prompts can be changed instantly and for free

The rest is Pro only

Week 1 is free for everyone. Week 2 onwards — plus mock exams and unlimited review — is included in the Pro plan.

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PreviousHow LLMs Work: Tokens, Embeddings, Context Window, and InferenceWeek 3 · Day 2Next Limitations and Risks of Generative AI: Hallucinations, Bias, Non-determinism, and Appropriate Use CasesWeek 3 · Day 4