The rise of LLMs has made the Retrieval Augmented Generation (RAG) framework popular for building question-answering systems. However, without proper tuning and experimentation, these systems may not be reliable in production. This article explores the problems with the RAG framework and provides tips for improving its performance, including leveraging document metadata and fine-tuning hyperparameters.
**Why Your RAG Is Not Reliable in a Production Environment**
*And how you should tune it properly*
With the rise of LLMs, the Retrieval Augmented Generation (RAG) framework has gained popularity in building question-answering systems over data.
While these systems are impressive, they may not be reliable in production without proper tweaking and experimentation.
In this post, we explore the problems with the RAG framework and share tips to improve its performance. From leveraging document metadata to fine-tuning hyperparameters, we provide practical solutions to enhance your RAG system.
RAG in a nutshell
Let’s start with the basics.
RAG works by taking an input question and retrieving relevant documents from an external database. It then uses those chunks of text as context for a language model (LLM) to generate an answer.
In simple terms, RAG tells the LLM, “Here’s my question and some text to help you understand. Give me an answer.”
However, RAG involves several components behind the scenes, including loaders to parse external data, splitters to chunk the data, an embedding model to convert the chunks into vectors, and a vector database to store and query them.
The problems with RAG
If you start building RAG systems without proper tuning, you may encounter some issues:
1. The retrieved documents are not always relevant to the question, leading to repetitive answers.
2. RAG systems lack basic world knowledge, sometimes providing inaccurate or invented facts.
3. RAG can be slow, impacting the user experience.
4. The process is lossy, gradually losing information from the external documents.
Tips to improve RAG performance
To address these issues, here are some practical tips:
1. Inspect and clean your data to ensure its quality and consistency.
2. Finetune the chunk size, top_k, and chunk overlap parameters for optimal results.
3. Leverage document metadata to filter and refine the retrieved documents.
4. Tweak your system prompt to set a default behavior or specific instructions for the RAG.
5. Transform the input query if needed to improve context and relevance.
Conclusion
To make your RAG system reliable and suitable for production, it’s essential to address the issues and implement the suggested tips. As AI technology continues to advance, optimization techniques will emerge, making RAG more reliable and ready for industrialized applications.
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