Understanding Retrieval-Augmented Generation (RAG)

Key Analogy: Journalist and Librarian

• Journalist = User (e.g., Business Analyst)
  • Needs up-to-date, relevant information for an article
• Librarian = Vector Database
  • Expert on book content and retrieves relevant books for the journalist

Application to RAG

Scenario Breakdown

1. User/Journalist asks a question
   • Example: Business Analyst asking, "What was revenue in Q1 from customers in the Northeast?"
2. Vector Database/Librarian retrieves relevant data
   • Structured and unstructured data aggregation
3. Large Language Model (LLM) generates an output
   • Uses vector embeddings to produce a precise answer

Steps in RAG Process

• Prompting: Receipt of user’s question
• Querying Vector Database: Retrieval of data embeddings
• Combining Embeddings with Prompt: Enhances the prompt with key data
• LLM Response: Generates the output

Benefits and Challenges

Benefits

• Aggregates multiple sources (e.g., PDFs, Apps, Images) for accurate answers
• Fetches up-to-date and accurate data continually

Challenges

• Accuracy: Risk of hallucinations and biases in LLM-generated results
• Data Governance: Need for clean, managed data fed into vector databases
• Transparency: LLMs must be transparent in their training data and processes

Solutions to Challenges

Data Quality and Governance

• Ensuring data is clean, governed, and managed
• Garbage In, Garbage Out: Importance of good database inputs

Transparent LLMs

• Avoid black-box models
• Ensure training data is free from IP issues and biases

Conclusion

• Trust in data similar to trust in books in a library is key
• Combining good governance, data management, and transparent AI models is crucial for building reliable, customer-facing AI applications