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.

AI Systems Insights

Field notes on RAG, agents, and production AI. Real-world implementations and clear thinking.

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Self-Correcting RAG with LangGraph: Build AI That Fixes Its Own Mistakes (2025)

Learn how to build self-correcting RAG systems using LangGraph and LangChain. This guide covers reflection patterns, agentic workflows, and the best RAG frameworks for building custom AI solutions that improve their own responses.

9 min read 2025-06-01

Filtering by: Self-Improving RAG

Self-Correcting RAG

2025-06-01 9 min

Self-Correcting RAG with LangGraph: Build AI That Fixes Its Own Mistakes (2025)

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