GenAI Reality Check: That RAG Use Case?

 

Keep Thinking!!!

Understanding Index RAG: Data Storage vs. Retrieval

In the realm of information retrieval and artificial intelligence, Index RAG (Retrieval-Augmented Generation) has emerged as a powerful technique. To fully grasp its potential and limitations, it's crucial to understand the distinction between data storage and retrieval, particularly in the context of indexing strategies. This post will explore two different indexing approaches and their implications for handling queries, especially multipart questions.

The Indexes

Index 1: Broad and Diverse

Composition: 20 pages from history + 20 pages from geography + 20 pages from maths

Strengths:

• Versatility: Covers multiple subjects, enabling efficient responses to multipart questions
• Diversity: Offers a well-rounded breadth of content across different fields

Index 2: Deep and Focused

Composition: 200 pages focused solely on history

Strengths:

• In-Depth Knowledge: Provides comprehensive depth on history, ideal for complex historical inquiries
• Rich Content: More pages dedicated to one subject increases potential for detailed responses

Trade-offs

Breadth vs. Depth

• Index 1: Offers breadth across subjects but may lack depth for in-depth analysis
• Index 2: Delivers depth in history but falls short on breadth for interdisciplinary queries

Complexity of Queries

• Index 1: Can handle complex, multipart questions effectively due to subject variety
• Index 2: May struggle with multipart questions spanning multiple disciplines

Information Quality

• Index 1: Information may be less densely packed with specialized detail
• Index 2: Provides rich historical data but lacks subject diversity

Challenges with Multipart Questions

Consider a multipart question involving history and mathematics:

Using Index 1:

• Pros: Can provide relevant information across both subjects
• Cons: Detail may not be as profound, potentially leading to surface-level insights

Using Index 2:

• Pros: Historical aspect might be well-covered
• Cons: Absence of mathematical content results in an incomplete answer

Implications for RAG Systems

Query Processing:

• RAG systems using Index 1 may need sophisticated algorithms to balance information from different domains
• Systems using Index 2 might require additional steps to supplement missing interdisciplinary information

Content Generation:

• Index 1 allows for more flexible content generation across topics
• Index 2 enables deep, nuanced responses within its specialized domain

System Architecture:

• Index 1 might benefit from a modular architecture that can efficiently combine information from different subjects
• Index 2 could leverage specialized language models fine-tuned for historical content

Conclusion

The choice between a broad, versatile index (Index 1) and a deep, focused index (Index 2) significantly impacts the retrieval effectiveness of an information system. Understanding these dynamics is crucial for users and developers alike to create effective RAG systems.

When designing or using RAG systems, consider:

• The nature of expected queries (single-domain vs. interdisciplinary)
• The required depth of information
• The system's ability to synthesize information from multiple sources

By carefully weighing these factors, one can optimize the balance between data storage and retrieval capabilities in Index RAG systems, ultimately enhancing the quality and relevance of generated responses.

KG vs RAG

• First, we solved with Prompt
• Next, we solved with RAG
• Next, we did Summary, RAG
• Next, we moved to Txt2SQL

There is some leftover space for Graph

Build a product and use tech according to needs, No Forefit in the equation :)

Keep Exploring!!!

Experimentation, Always something to find a solution :)

Some questions and answers take days or weeks, and sometimes the approach moves from LLM to NLP, It's a blend of techniques to make things work.

• How do we optimize RAG with internal documents, Original vs Summary vs Intents, What works best? 
• How do we merge external data? How can we keep versions and relevance?
• More than LLM work, The heavy lifting is for Data preprocessing/cleaning / Embedding on summary 
• When to use LLM vs Multimodals?
• What is the benchmark for our domain and how much do we meet it consistently?
• The transition for LLM, LLM+KG, Creating the data mapping..

A lot of challenges but one at a time, Balancing Consistency, Accuracy, and Latency. If you want to solve real problems you can connect/explore potential learning experimentation opportunities / dedicate some learning hours. Please drop a note to career@proplens.ai

#learnings #NLP #Datascience #RAG #LLMs #perspectives #Datascience 

Keep Exploring!!!

RAG and Prompts - Learning Evolves

In one particular use case, it's a constant process of experimentation and iterations.

• Step #1 - Let me try with prompts - It works but not consistently
• Step #2 - Let me try with a vision prompt - It takes time. 
• Step #3 - Let me merge everything in the database and check. 
• Step #4 - Routing takes 2 seconds, querying takes 2 seconds, and prompting takes 2 seconds. 
• Step #5 - Let's keep accuracy and latency separate, divide everything into separate tracks, and sort out the basics.

Some successes, some lessons, and some learning.

Keep Exploring!!!