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The Chronicles of RAG: The Retriever, the Chunk and the Generator
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Retrieval Augmented Generation (RAG) has become one of the most popular paradigms for enabling LLMs to access external data, and also as a mechanism for grounding to mitigate against hallucinations. When implementing RAG you can face several challenges like effective integration of retrieval models, efficient representation learning, data diversity, computational efficiency optimization, evaluation, and quality of text generation. Given all these challenges, every day a new technique to improve RAG appears, making it unfeasible to experiment with all combinations for your problem. In this context, this paper presents good practices to implement, optimize, and evaluate RAG for the Brazilian Portuguese language, focusing on the establishment of a simple pipeline for inference and experiments. We explored a diverse set of methods to answer questions about the first Harry Potter book. To generate the answers we used the OpenAI's gpt-4, gpt-4-1106-preview, gpt-3.5-turbo-1106, and Google's Gemini Pro. Focusing on the quality of the retriever, our approach achieved an improvement of MRR@10 by 35.4% compared to the baseline. When optimizing the input size in the application, we observed that it is possible to further enhance it by 2.4%. Finally, we present the complete architecture of the RAG with our recommendations. As result, we moved from a baseline of 57.88% to a maximum relative score of 98.61%.
Forward citations
Cited by 6 Pith papers
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On WeChat's closed-source codebase, similarity-based RAG with combined BM25 and GTE-Qwen retrieval improves open-source LLM code completion more than identifier-based retrieval, with gains growing for larger models.
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DyG-RAG: Dynamic Graph Retrieval-Augmented Generation with Event-Centric Reasoning
DyG-RAG builds a dynamic event graph from time-anchored event units and uses timeline retrieval with Time-CoT prompting to answer temporal questions.
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Markov-Enhanced Clustering for Long Document Summarization: Tackling the 'Lost in the Middle' Challenge with Large Language Models
A Markov-chain ordered clustering pipeline improves reported ROUGE and coherence over direct LLM summarization on BookSum, with small unverified gains.
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