Curriculum Overview: Design Considerations for Foundation Model Applications

This curriculum is designed to provide a comprehensive framework for architecting, optimizing, and deploying applications powered by Foundation Models (FMs) within the AWS ecosystem. It focuses on the strategic trade-offs between cost, performance, and accuracy.

Prerequisites

Before engaging with this curriculum, learners should possess the following foundational knowledge:

• Cloud Computing Fundamentals: Understanding of AWS global infrastructure (Regions, Availability Zones) and core services (IAM, VPC, S3).
• AI/ML Basics: Familiarity with the machine learning development lifecycle (Data collection $\to$ Training $\to$ Evaluation $\to Deployment).
• Generative AI Core Concepts: Understanding of tokens, embeddings, and the Transformer architecture.
• Basic Programming: Knowledge of Python and API interaction (REST/JSON).

Module Breakdown

Module Objectives

Module 1: Model Selection & Criteria

• Identify Selection Criteria: Evaluate models based on modality (text-to-image vs. text-to-text), latency requirements, and multilingual support.
• Complexity Analysis: Contrast model size (parameters) with reasoning capabilities and operational costs.
• Licensing & Compliance: Understand the implications of open-source vs. proprietary model licenses (e.g., via Bedrock Model Profiles).

Module 2: Inference & Response Control

• Parameter Tuning: Explain how Temperature$(randomness) and$Top-P$ (nucleus sampling) affect creativity vs. determinism.
• Token Management: Calculate cost and performance impacts of input/output length and prompt caching.
• Hallucination Mitigation: Implement strategies to manage non-deterministic outputs and Plausible-but-incorrect fabrications.

Module 3: Retrieval-Augmented Generation (RAG)

• Data Augmentation: Define RAG as a method to provide external, domain-specific context without modifying model weights.
• Vector Infrastructure: Identify AWS services for embedding storage, including Amazon OpenSearch Service, Amazon Aurora (pgvector), and Amazon Neptune.
• Integration: Use Amazon Bedrock Knowledge Bases to automate the RAG workflow.

Module 4: Customization Approaches

• Cost-Benefit Analysis: Compare the trade-offs between In-context Learning (prompting), Fine-tuning (updating weights), and Pre-training from scratch.
• Optimization Techniques: Describe Distillation (transferring knowledge from large to small models) and Instruction Tuning.

Module 5: Agentic AI & Multi-step Tasks

• Role of Agents: Describe how Amazon Bedrock Agents execute complex tasks by calling external APIs and data sources.
• Protocol Mastery: Understand the Model Context Protocol for standardized tool-use communication.

Success Metrics

To evaluate the efficacy of FM-based applications, the following metrics must be tracked:

[!IMPORTANT] Technical Metrics vs. Business Metrics Always align model performance (ROUGE/BLEU) with business value (Conversion Rate/Efficiency).

1. Technical Performance:

   • ROUGE/BLEU Scores: Measuring text similarity for summarization and translation.
   • Latency: Time to First Token (TTFT) and total response time.
   • Perplexity: Assessing how well the model predicts the sample.

2. Business Impact:

   • Accuracy & Hallucination Rate: Frequency of factually incorrect outputs in production.
   • Conversion Rate: Percentage of users completing a task via the AI assistant.
   • Customer Lifetime Value (CLV): Long-term impact of AI-driven personalization on user retention.

Real-World Application

Foundation models are transforming industries through specific deployment patterns:

• Customer Support: Utilizing RAG to answer proprietary questions about company policy using internal knowledge bases.
• Content Generation: Automating marketing copy and blog posts while maintaining brand voice via fine-tuning.
• Coding Assistants: Using Large Language Models (LLMs) like Amazon Q to assist developers in debugging and code generation.
• Data Automation: Leveraging Amazon Bedrock Data Automation to extract structured insights from unstructured documents.

▶Click to view: The "Muddiest Point" - RAG vs. Fine-tuning

One of the most common points of confusion is when to use RAG versus Fine-tuning.

• RAG is for providing new facts (knowledge).
• Fine-tuning is for changing the style, format, or behavior (learning how to talk).