3.5 Effective Prompt Engineering Techniques
1. What is a Prompt? 2. Types of Prompting Techniques: 3. What is Prompt Engineering? 4. Common Tasks Supported by…
3.4 RAG, Vector Databases, and Agents
1. RAG (Retrieval Augmented Generation): 2. Using Vector Databases in the Real World: 3. Hallucinations in LLMs: 4. Amazon Bedrock…
3.3 Inference Parameters and Prompt Engineering
1. Inference and Inference Parameters: 2. Amazon Bedrock Inference Parameters: 3. Finding the Optimal Balance: 4. Prompt Engineering: 5. Vector…
3.2 Considerations for Pre-Trained Models
1. Bias in Training Data: 2. Availability and Compatibility of Pre-Trained Models: 3. Customization and Explainability: 4. Interpretability vs. Explainability:…
3.1 Design considerations for Foundation Models
This task focuses on design considerations when using foundation models in applications. The key factors to consider when selecting pre-trained…
Quiz 2
Question 1: A retail company wants to start testing Amazon Bedrock foundation models (FMs) for text generation and customer-facing natural…
2.8 Understanding Cost Tradeoffs for AWS Gen AI Services
When using Large Language Models (LLMs), there are two main pricing models: AWS Global Infrastructure AWS offers a globally resilient…
2.7 AWS for Building Generative AI Applications
AWS offers several advantages for building generative AI applications, including: Transfer Learning for Faster Model Training AI model training can…
2.6 Choosing and Evaluating Generative AI Models
When selecting a generative AI model, several factors need to be considered: Types of Generative AI Models Generative AI foundation…
2.5 Model Fine Tuning and Performance
Fine-Tuning for Better Performance Fine-tuning helps improve LLM performance by making them understand human-like prompts and generate natural-sounding language. However,…