What is RAG (Retrieval-Augmented Generation)?

RAG combines retrieval of relevant information from knowledge bases with generative AI to provide accurate, contextual responses. It enables AI systems to access up-to-date information beyond their training data.

How much does custom AI chatbot development cost?

Custom AI chatbot development costs vary based on complexity, features, and integration requirements. Basic chatbots start from $5,000, while enterprise-grade RAG systems range from $15,000-$50,000+.

LangChain vs LlamaIndex: Which is better?

LangChain excels at complex workflows and agent chains, while LlamaIndex specializes in data indexing and retrieval. Choose LangChain for multi-step processes, LlamaIndex for document-heavy applications.

What are the benefits of Multimodal RAG?

Multimodal RAG processes text, images, and documents together, enabling comprehensive understanding of mixed-media content. It's ideal for technical documentation, medical records, and visual data analysis.

Blog - AI Insights & Tutorials

Building Multimodal RAG Systems with CLIP and Pinecone

This blog post explores how to build a powerful multimodal Retrieval-Augmented Generation (RAG) system using CLIP and Pinecone vector database.

📖 8 min read 📅 2025-08-27

AI Blogs & Insights

Expert Knowledge on RAG, Multimodal RAG, AI Agents, and more

Building Multimodal RAG Systems with CLIP and Pinecone