A Guide on 12 Tuning Strategies for Production-Ready RAG Applications
This article covers the following “hyperparameters” sorted by their relevant stage. In the ingestion stage of a RAG pipeline, you can achieve performance improvements by: Data cleaningChunkingEmbedding modelsMetadataMulti-indexingIndexing algorithmsAnd in the inferencing stage (retrieval and generation), you can tune: Query transformationsRetrieval parametersAdvanced retrieval strategiesRe-ranking
Mastering Customer Segmentation with LLM
Let’s see a brief description of the columns of our dataset: age (numeric)job : type of job (categorical: “admin.” ,”unknown”,”unemployed”, ”management”, ”housemaid”, ”entrepreneur”, ”student”, “blue-collar”, ”self-employed”, ”retired”, ”technician”, ”services”)marital : marital status (categorical: “married”,”divorced”,”single”; note: “divorced” means divorced or widowed)education (categorical: “
Fine-Tune Your Own Llama 2 Model in a Colab Notebook
Image by authorWith the release of LLaMA v1, we saw a Cambrian explosion of fine-tuned models, including Alpaca, Vicuna, and WizardLM, among others. This trend encouraged different businesses to launch their own base models with licenses suitable for commercial use, such as OpenLLaMA, Falcon, XGen, etc. The release of Llama 2 now combines the best elements from both sides: it offers a highly effic
10 Ways to Improve the Performance of Retrieval Augmented Generation Systems
The Quick-start Guide Isn’t Enough“Retrieval augmented generation is the process of supplementing a user’s input to a large language model (LLM) like ChatGPT with additional information that you (the system) have retrieved from somewhere else. The LLM can then use that information to augment the response that it generates.” — Cory Zue LLMs are an amazing invention, prone to one key issue. They mak
Testing Language Models (and Prompts) Like We Test Software
Image created by the authors.How can we test applications built with LLMs? In this post we look at the concept of testing applications (or prompts) built with language models, in order to better understand their capabilities and limitations. We focus entirely on testing in this article, but if you are interested in tips for writing better prompts, check out our Art of Prompt Design series (ongoing
Not All Vector Databases Are Made Equal
While working on this blog post I had a privilege of interacting with all search engine key developers / leadership: Bob van Luijt and Etienne Dilocker (Weaviate), Greg Kogan (Pinecone), Pat Lasserre, George Williams (GSI Technologies Inc), Filip Haltmayer (Milvus), Jo Kristian Bergum (Vespa), Kiichiro Yukawa (Vald) and Andre Zayarni (Qdrant) This blog has been discussed on HN: https://news.ycombi
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