3.10 Integration of LLMs into Applications
1. Questions for Integration into Applications: 2. Retrieval-Augmented Generation (RAG): 3. Handling Outdated Knowledge: 4. Business Objectives and Application Design:…
3.9 Evaluating Foundation Model Performance
1. Questions to Consider for Model Integration: 2. Inference Challenges: 3. Optimization Techniques: 4. Evaluation Metrics for Generative AI: 5.…
3.8 Preparing Data for Fine-Tuning Foundation Models
1. Preparing Training Data: 2. Fine-Tuning Process: 3. Data Preparation in AWS: 4. Continuous Pre-training:
3.7 Training and Fine-Tuning Foundation Models
1. Key Elements of Training a Foundation Model: 2. Difference Between Pre-training and Fine-tuning: 3. Challenges with Fine-tuning: 4. PEFT…
3.6 Prompt Engineering and Latent Space
1. Latent Space and Language Models: 2. Hallucinations and Model Limitations: 3. Key Techniques in Prompt Engineering: 4. Prompt Engineering…
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…