English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Retrieval Augmented Generation (RAG) has become one of the most popularparadigms for enabling LLMs to access external data, and also as a mechanismfor grounding to mitigate against hallucinations. When implementing RAG you canface several challenges like effective integration of retrieval models,efficient representation learning, data diversity, computational efficiencyoptimization, evaluation, and quality of text generation. Given all thesechallenges, every day a new technique to improve RAG appears, making itunfeasible to experiment with all combinations for your problem. In thiscontext, this paper presents good practices to implement, optimize, andevaluate RAG for the Brazilian Portuguese language, focusing on theestablishment of a simple pipeline for inference and experiments. We explored adiverse 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 theretriever, our approach achieved an improvement of MRR@10 by 35.4% compared tothe baseline. When optimizing the input size in the application, we observedthat it is possible to further enhance it by 2.4%. Finally, we present thecomplete architecture of the RAG with our recommendations. As result, we movedfrom a baseline of 57.88% to a maximum relative score of 98.61%.

The Chronicles of RAG: The Retriever, the Chunk and the Generator