Building the World's Most Accurate AI-Powered Document Retrieval Platform: A 4-Year Journey

As the co-founder of il, I've spent 15 years building AI-powered applications, and now I'm excited to share our innovative approach to achieving industry-leading accuracy in Large Language Models (LLMs) for real-world document retrieval.

1. Ben is a co-founder of il, which builds AI-powered applications.
2. He has been working in this field for 15 years, starting at IBM research and then IBM Watson.
3. Il has developed the world's most accurate and scalable RAG (Retrieval Augmented Generation) platform using no-code tools and APIs.
4. Users can upload documents and receive accurate retrievals within minutes.
5. Il has been developing its solution for four years and was an early user of the GPT3 beta program.
6. RAG can have high error or hallucination rates, especially with complicated enterprise documents.
7. The source of these errors is usually not the language models or prompts, but RAG itself or the quality and relevance of retrieved content.
8. Content-related problems typically fall into three categories: bad text extraction, missing information, and non-extracted visual elements.
9. Most issues with RAG are related to content ingestion and can take hundreds of hours to solve.
10. Il has spent four years addressing these data engineering challenges and has built solutions into its ingestion pipeline.
11. As a result, users can build accurate RAG applications in just minutes.
12. Il's platform has achieved 98% accuracy against complicated real-world documents, outperforming some popular solutions by as much as 120%.
13. Il does not use vector databases, which they believe may not be the best technology for many RAG applications.
14. Instead, il creates what they call semantic objects and performs a multi-field search across their attributes.
15. Semantic objects include the original chunk text and auto-generated metadata that preserves information around the text.
16. Il rewrites the text into two ideal formats: one for search and another for completion.
17. For example, Air France has been using il's platform to build a chat GPT-like copilot for their call center agents, with a knowledge base of hundreds of thousands of documents.
18. Il's ingestion pipeline identifies images, tables, and text in the documents and processes them through dedicated multimodal processing pipelines.
19. Rewriting the query into a format compatible with semantic objects allows il to search the entire object: original text, auto-generated metadata, and search version of the text.
20. Il uses fine-tuned language models to rank results and improve accuracy.
21. In total, more than nine models are fine-tuned for ingestion and search in il's platform.
22. The end result is the world's most accurate RAG platform, enabling users to build enterprise-quality production-ready applications in minutes instead of months.
23. Il invites users to try their platform at il.la/xray.
24. Ben concluded his presentation by thanking the audience.

Source: AI Engineer via YouTube

❓ What do you think? What are the most significant challenges in building a high-accuracy Rag application, and how can we overcome these hurdles to achieve better performance? Feel free to share your thoughts in the comments!