The Allure of RAG

• Compute Embeddings: Break down your documents into chunks and convert them into embeddings—numerical representations that capture the essence of the text.
• Store and Retrieve: Keep these embeddings in a database. When a question comes in, find the chunks whose embeddings are most similar to the question.
• Augment the AI: Feed these relevant chunks to the AI alongside the question, giving it the context it needs to generate an informed answer.

The Reality Check

1. The Ever-Changing Embeddings

• Recompute All Embeddings: Every time a new model comes out, you could reprocess your entire document library to generate new embeddings. But if you're dealing with millions or billions of chunks, that's a hefty computational bill.
• Stick with the Old Model: You might decide to keep using the old embeddings to save on costs. But over time, you miss out on improvements and possibly pay more for less efficient models.
• Mix and Match: Use new embeddings for new documents and keep the old ones for existing data. But now your database is fragmented, and searching across different embedding spaces gets complicated.

2. The Pronoun Problem

Solving It

3. Navigating Domain-Specific Knowledge

4. The Limits of Knowledge Graphs

The Complexity of Real-World Information

Conflicting Information

5. The Human vs. Machine Conundrum

The Database Dilemma

So, What Now?

• Develop Smarter Preprocessing: Clean and prepare text in ways that make it easier for AI to understand, like resolving pronouns and simplifying sentences.
• Embrace Hybrid Approaches: Combine embeddings with other methods, like traditional search algorithms or domain-specific rules, to improve accuracy.
• Accept Imperfection: Recognize that AI has limitations and set realistic expectations about what it can and can't do.

Final Thoughts