Building Production-Ready RAG Applications: Jerry Liu
AI Engineer・2 minutes read
Jerry from L index discusses building production-ready RAG applications and announces a bucket hat raffle. Different paradigms for training language models on new data are compared, with a focus on RAG systems and ways to improve performance through optimization and evaluation.
Insights
- Building production-ready rag applications involves understanding the two main paradigms for training language models: retrieval augmentation and fine-tuning, with a focus on optimizing data, retrieval algorithms, and synthesis methods for improved performance.
- Enhancing RAG systems can be achieved through advanced techniques like small to big retrieval, tuning chunk sizes, and embedding references to parent trunks, while exploring llms for reasoning beyond synthesis opens up possibilities for multi-document agents that can summarize documents, perform QA, and retrieve specific facts.
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Recent questions
How can beginners build a QA system?
In around five lines of code.
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Summary
00:00
Building Production-Ready RAG Applications: Techniques and Challenges
- Jerry, co-founder and CEO of L index, discusses building production-ready rag applications and announces a raffle for a bucket hat at their booth.
- Various use cases in AI, like knowledge search and conversational agents, can be built using llms' reasoning capabilities.
- Two main paradigms for training language models on new data: retrieval augmentation and fine-tuning.
- RAG (Retrieval Augmented Generation) stack for QA systems involves data ingestion, retrieval, and synthesis components.
- Beginners at L index can build a QA system in around five lines of code, but understanding lower-level components is encouraged.
- Challenges with naive RAG include response quality issues like bad retrieval, low precision, and outdated information.
- Ways to improve RAG performance include optimizing data, retrieval algorithms, and synthesis methods.
- Evaluation of RAG systems involves defining benchmarks, evaluating retrieval and synthesis components, and optimizing the system.
- Basic techniques for improving RAG systems include tuning chunk sizes and metadata filtering.
- Advanced techniques like small to big retrieval can enhance retrieval quality by embedding smaller text chunks for synthesis.
13:24
Optimizing Retrieval and Synthesis with llms
- Setting a smaller top K value like k equal 2 can help avoid loss in the middle problems during retrieval over big text chunks.
- Embedding a reference to the parent trunk instead of the actual text chunk has been found to improve retrieval performance significantly.
- Exploring the use of llms for reasoning beyond synthesis involves considering multi-document agents that can summarize documents, perform QA, and retrieve specific facts.
- Fine-tuning in a rag system involves optimizing specific parts of the pipeline, such as embeddings, to enhance retriever and synthesis capabilities, potentially using synthetic query datasets generated from llms.
