Vector databases power the retrieval layer in RAG workflows by storing document and query embeddings as high‑dimensional vectors. They enable fast similarity searches based on vector distances.| AIMultiple
I have been relying on SQL for data analysis for 18 years, beginning with my days as a consultant. Translating natural-language questions into SQL makes data more accessible, allowing anyone, even those without technical skills, to work directly with databases.| AIMultiple
LLMs are growing rapidly, but development and fine-tuning remain expensive.1 | AIMultiple
Dense vector search is excellent at capturing semantic intent, but it often struggles with queries that demand high keyword accuracy. To quantify this gap, we benchmarked a standard dense-only retriever against a hybrid RAG system that incorporates SPLADE sparse vectors.| AIMultiple
The effectiveness of any Retrieval-Augmented Generation (RAG) system depends on the precision of its retriever component.| AIMultiple
This article gathers the most common AI use cases covering marketing, sales, customer services, security, technology, and other processes.| AIMultiple
Discover & compare top 20 AI governance tools to shortlist best AI governance software for your business, & start build & deploy responsible AI.| AIMultiple
Data-driven, practical, transparent insights to help enterprises identify new AI & software tools in automation & cybersecurity to accelerate their businesses| AIMultiple
See the top AI agent builders & tools based on their specialization and discover their key features: CrewAI, Camel, Beam AI, Autogen, LangGraph, ChatDev, Lidy, AIlice| AIMultiple
Large language models (LLMs) have generated much hype in recent months (see Figure 1). The demand has led to the ongoing development of websites and solutions that leverage language models. ChatGPT set the record for the fastest-growing user base in January 2023, proving that language models are here to stay. This is also shown by the fact that Bard, Google’s answer to ChatGPT, was introduced in February 2023.| AIMultiple
RAG (Retrieval-Augmented Generation) improves LLM responses by adding external data sources. We benchmarked different embedding models with various chunk sizes to see what works best.| AIMultiple
AI models sometimes generate data that seems plausible but is incorrect or misleading; known as AI hallucinations. According to Deloitte, 77% of businesses| AIMultiple
More than 37% of tasks performed on AI models are about computer programming and| AIMultiple
We analyzed 15+ LLMs and their pricing and performance. LLM API pricing can be complex and depends on your preferred usage. If you plan to use:| AIMultiple
Check out this guide to finding the right price monitoring tools for your competitive business.| AIMultiple
Explore 21 BPM statistics about BPM market size, adoption rates, vendors, benefits & challenges.| AIMultiple: High Tech Use Cases & Tools to Grow Your Business
Explore 42+ up to date low code stats about the market, adoption, app development, benefits, case studies, use cases & vendor funding| AIMultiple: High Tech Use Cases & Tools to Grow Your Business
AI chips enable parallel computing capabilities are increasingly in demand. This article will information to you on 10 popular AI chip makers.| AIMultiple
In this article, we have gathered the top 70+ generative AI applications that can be used in general or for industry-specific purposes.| AIMultiple: High Tech Use Cases & Tools to Grow Your Business
Explore 90+ up-to-date chatbot stats from surveys: market size, adoption, customer perspective, benefits, challenges & industry-specific stats| AIMultiple: High Tech Use Cases & Tools to Grow Your Business