How to Build Production-Ready RAG Applications

🔑Retrieval augmentation is a key technique to improve the performance of Rag systems.

🚀Tuning chunk sizes and implementing metadata filtering can significantly impact the performance of Rag systems.

💡Rag systems can be used for reasoning and not just generation, enabling more sophisticated applications.

🔍Evaluation of Rag systems is crucial and involves measuring retrieval and synthesis performance.

🔧Start with table stakes techniques before diving into more advanced optimizations for Rag systems.

What is retrieval augmentation?

—Retrieval augmentation is a technique that involves fixing the model and creating a data pipeline to add context from a data source into the input prompt of the language model.

How can chunk sizes impact Rag performance?

—Tuning chunk sizes can have significant impacts on Rag performance, as it affects the context window of the language model and the relevance of retrieved information.

Can Rag systems be used for reasoning?

—Yes, Rag systems can be used for reasoning, allowing for more sophisticated applications that go beyond simple generation.

How should Rag systems be evaluated?

—Rag systems should be evaluated based on retrieval and synthesis performance, measuring aspects like success rate, hit rate, and relevance of the generated response.

What are table stakes techniques for optimizing Rag systems?

—Table stakes techniques include tuning chunk sizes, implementing metadata filtering, and leveraging existing features in vector databases to enhance Rag performance.

00:01[Music]

00:14Jerry introduces the topic of building production-ready Rag applications.

01:10There are two main paradigms for getting language models to understand new data: retrieval augmentation and fine-tuning.

05:35Evaluation of Rag systems is important, involving both retrieval and synthesis measurements.

09:55Tuning chunk sizes and implementing metadata filtering can have a significant impact on the performance of Rag systems.

10:52Rag systems can be used for reasoning, going beyond simple generation.

10:59Optimizing Rag systems involves starting with table stakes techniques before exploring more advanced optimizations.