How MIT Built a Zero-Hallucination RAG System Without a Dev Team
MIT's Martin Trust Center deployed a Retrieval-Augmented Generation (RAG) chatbot called ChatMTC using the no-code platform CustomGPT.ai. The article discusses the challenges of building a production-grade RAG system and how MIT overcame the problem of multimodal data ingestion and hallucinations.
Why it matters
This case study demonstrates how no-code platforms can help organizations quickly deploy production-grade AI chatbots without the need for a large engineering team.
Key Points
- 1MIT had a massive library of unstructured
- 2 including PDFs, websites, and video transcripts
- 3CustomGPT.ai handled the data ingestion pipeline, converting everything into a unified vector space
- 4MIT implemented strict source-grounded logic to prevent hallucinations, where the model can only use the provided context
- 5ChatMTC outperformed the legacy help desk in response time, language support, accuracy, and DevOps overhead
Details
Building a Retrieval-Augmented Generation (RAG) pipeline from scratch is not a simple task, as it involves dealing with document parsing, chunking strategies, vector databases, embeddings, orchestration, and UI/API layers. MIT's Martin Trust Center (MTC) had a large library of unstructured
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