Customizing Foundation Models and Retrieval Augmented Generation

This article delves into the importance of customizing foundation models, which are pre-trained machine learning models that can be fine-tuned for specific tasks. The article explores the common approaches used for customization, such as transfer learning, prompt engineering, and model fine-tuning.

One of the key techniques discussed is retrieval augmented generation (RAG), a method for improving the quality and accuracy of generative AI responses by leveraging external knowledge sources. RAG allows models to access and incorporate relevant information from knowledge bases, which can enhance the coherence, factual accuracy, and overall quality of the generated output.

Why Customize Foundation Models?

• Foundation models have vast pre-trained knowledge, but may not know specifics about your company or industry
• Customization can help:
  • Adapt to domain-specific language
  • Improve performance on unique, company-specific tasks
  • Improve context and awareness with your own data

Common Customization Approaches

1. Prompt Engineering:
   • Prompt priming
   • Prompt weighting
   • Prompt chaining
2. Retrieval Augmented Generation (RAG):
   • Retrieve relevant text from documents
   • Use as context for foundation model
3. Model Fine-tuning:
   • Adapt foundation model on specialized, labeled data
4. Training from Scratch:
   • Build a domain-specific model with full control over training data

Retrieval Augmented Generation (RAG)

Retrieval Augmented Generation (RAG) is a technique that combines the power of information retrieval and language generation to produce more coherent and informative responses.

The process involves three key steps:

1. Retrieval: Relevant information is fetched from a knowledge base to provide context and background for the task at hand.
2. Augmentation: The retrieved information is combined with the original query or prompt to create a more comprehensive and informed input for the language mode
3. Generation: The foundation model then uses the augmented prompt to generate a response, leveraging the additional context to produce a more accurate and relevant output.

RAG has several promising use cases:

• Improving content quality by reducing hallucinations (the generation of factually incorrect information).
• Building context-based chatbots and question-answering systems that can provide more informed and personalized responses.
• Enhancing personalized search by incorporating relevant background information into the search results.
• Improving text summarization by drawing on external knowledge to produce more comprehensive and informative summaries.

Implementing RAG

The data ingestion workflow involves several steps:

• Extract text from various data sources, such as S3, PDFs, and CSVs.
• Chunk the data and create embeddings using the Titan text model.
• Store the embeddings in a vector database.

The retrieval workflow consists of the following steps:

• Convert the user’s query into embeddings.
• Perform a vector similarity search to retrieve the relevant data chunks.
• Augment the prompt with the retrieved chunks.
• Pass the augmented prompt to the foundation model for generation.