Building Production-Ready RAG Applications: Jerry Liu

So far the most completed and clear LLM RAG go-through video ever existed on Youtube.

00:00:49 Fix the model by creating a data pipeline to add context into the prompt. 00:01:33 Understand the paradigms of retrieval augmentation and fine-tuning for language models. 00:02:00 Learn about building a QA system using data ingestion and querying components. 00:02:07 Explore lower-level components to understand data ingestion and querying processes. 00:03:01 Address challenges with naive rag applications, such as poor response quality. 00:04:02 Improve retrieval performance by optimizing data storage and pipeline. 00:04:14 Enhance the embedding representation for better performance. 00:04:45 Implement advanced retrieval methods like reranking and recursive retrieval. 00:05:18 Incorporate metadata filtering to add structured context to text chunks. 00:06:27 Experiment with small to big retrieval for more precise retrieval results. 00:07:14 Consider embedding references to parent chunks for improved retrieval. 00:09:31 Explore the use of agents for reasoning and more advanced analysis. 00:12:12 Fine-tune the rag system to optimize specific components for better performance. 00:17:01 Generate a synthetic query dataset from raw text chunks using LLMS to fine-tune and embed a model. 00:17:12 Fine-tune the base model itself or fine-tune an adapter on top of the model to improve performance. 00:17:16 Consider fine-tuning an adapter on top of the model as it has advantages such as not requiring the base model's weights to fine-tune and avoiding the need to reindex the entire document corpus when fine-tuning the query. 00:18:00 Explore the idea of generating a synthetic dataset using a bigger model like GBD4 and distilling it into a weaker LM like 3.5 Turbo to enhance train of thought, response quality, and structured outputs.

Thank you not just for putting this together, but by making sense of it all! In 18min!? Amazing!

So far this is the best presentation on RAG I have ever come across in last couple of months.

i thoroughly enjoyed your presentation. jerry Liu-Thanks for the Deep methods to be applied to traditional RAG.-

Very deep talking! Really appreciate and learned a lot

I love Jerry's approach to identifying intuition and solution

Your distilled video has almost no knowledge loss over hours of coursework. Great work !

Thank you very much for this. In this age of LLms it is getting more and more important to be able to mesure theyr accuracy and efficacy. I've been working with problems like this since the beggining of 2024 and it's been such an interesting topic to learn about. Cheers and thx for the upload

I was thoroughly impressed by the depth of your insights and the clarity of your delivery. The ability of Jerry Liu to distill complex concepts into understandable terms was remarkable, and I particularly enjoyed how you illustrated the practical applications of RAG in various fields. Would it be possible for you to share the slides from the Jerry Liu's presentation?

short and sweet presentation. Very clear

Awesome rundown!

Really nice presentation skills, Jerry!

Thank you for this excellent presentation, very much appreciated

Very nice presentation and very practical tips for enterprise RAGs

Excellent presentation on RAG

Great one!

🎯 Key Takeaways for quick navigation: 01:44 🧩 The current RAG stack for building a QA system consists of two main components: data ingestion and data querying (retrieval and synthesis). 03:08 🚧 Challenges with naive RAG include issues with response quality, bad retrieval, low precision, hallucination, fluff in return responses, low recall, and outdated information. 04:31 🔄 Strategies to improve RAG performance involve optimizing various aspects, including data, retrieval algorithm, and synthesis. Techniques include storing additional information, optimizing data pipeline, adjusting chunk sizes, and optimizing embedding representation. 06:50 📊 Evaluation of RAG systems involves assessing both retrieval and synthesis. Retrieval evaluation includes ensuring returned content is relevant to the query, while synthesis evaluation examines the quality of the final response. 08:30 🛠️ To optimize RAG systems, start with "table stakes" techniques like tuning chunk sizes, better pruning, adjusting chunk sizes, and using metadata filters integrated with vector databases. 12:29 🧐 Advanced retrieval methods, such as small to big retrieval and embedding a reference to the parent trunk, can enhance precision by retrieving more granular information. 14:42 🧠 Exploring more advanced concepts, like multi-document agents, allows for reasoning beyond synthesis, enabling the modeling of documents as sets of tools for tasks such as summarization and QA. 16:23 🎯 Fine-tuning in RAG systems is crucial to optimize specific components, such as embeddings, for better performance. It involves generating synthetic query datasets and fine-tuning on either the base model or an adapter on top of the model. 18:15 📚 Documentation on production RAG and fine-tuning, including distilling knowledge from larger models to weaker ones, is available for further exploration. Made wit

Thanks for Your hard-work. Really learned a lot

wow thanks for the presentation