Unhobbling Retrieval-Augmented Generation with RAGLite

🚀 Are you disappointed by the quality and complexity of your RAG applications?

Retrieval-augmented generation (RAG) is one of the most commercially successful GenAI applications because it allows users to interact with an organization’s entire private knowledge base using the most natural interface: —language. Instead of sifting through search results, users get direct answers almost instantly.

However, traditional RAG setups come with major challenges:

🤯 Complexity – Requires multiple tools (LangChain, LlamaIndex, Rerankers, …)

🔥 High costs – Expensive infrastructure and API usage

🚧 Difficult maintenance – Many moving parts (Vector db, keyword db, backend, frontend, integrations, …)

👎 Disappointing output – Poor retrieval leads to low-quality answers

❌ Unmet user expectations – RAG applications solve “needle-in-the-haystack” problems, but users expect reasoning and analysis

What is RAGLite and how does it change the game?

In 2023 and 2024, we saw the “unhobbling” of RAG—the development of the best practices that result in the best RAG output quality. RAGLite is an open-source project by Superlinear that bundles all of these best practices in a single Python package, and additionally solves some of the last open problems in RAG:

✂️ Optimal document chunking – RAGLite produces (provably) optimal document chunks for RAG by solving an optimization problem.

🍰 Optimal fine-tuning of embeddings – RAGLite can fine-tune your embedding model with a single step to optimize the quality of the retrieved document chunks. RAGLite is the first package to provide the best solutions to each of the steps in a RAG pipeline in a single Python package. No need to stitch together multiple frameworks—RAGLite delivers everything in one place.

What you’ll learn in this webinar:

✅ How RAGLite simplifies retrieval-based AI applications

✅ The core architecture & design choices behind RAGLite

✅ Live demo: Building & optimizing a RAG pipeline

✅ Best practices for improving retrieval performance in real-world use cases

We’ll walk through both theory and hands-on examples, and end with a Q&A session to answer all your technical questions.

Practical information

📅 Date: March 26, 2025

⏰ Time: 11:30 AM - 12:30 PM CET

📍 Digital: Livestorm

🔗 Secure your spot now and start simplifying your RAG workflows!