1. Inference and Inference Parameters:
- Inference is the process of generating an output (prediction) from an input using a model.
- Inference parameters control the model’s behavior, including:
- Randomness (temperature)
- Diversity (Top K, Top P)
- Response length (length, penalties, stop sequences)
2. Amazon Bedrock Inference Parameters:
- Temperature: Controls randomness.
- Top K, Top P: Control diversity.
- Response length: Limits how long the model’s output will be.
- Penalties & stop sequences: Used to refine the output’s length and content.
3. Finding the Optimal Balance:
- Experiment with parameters to balance diversity, coherence, and resource efficiency.
- Continuously monitor and adjust parameters in production to maintain optimal performance.
4. Prompt Engineering:
- Prompts are the inputs provided to the model to generate the appropriate response.
- Retrieval Augmented Generation (RAG): Enhances prompts by adding domain-specific or internal data from databases.
- RAG helps models retrieve external knowledge to improve responses.
5. Vector Databases vs. Machine Learning Models:
- Vector Databases store data as mathematical representations (vectors).
- Vector Embeddings: Convert data like text or images into numbers to represent meaning.
- A machine learning model is used to create vector embeddings.
- Vector databases enhance model performance by storing and retrieving relevant data.
6. Role of Vector Databases in Foundation Models:
- Provide external data sources for better search, recommendations, and text generation.
- Add capabilities for data management, fault tolerance, authentication, and query engines.
