MobileRead Forums - View Single Post - Do builtin AI prompts need explict 'say I don't know' instruction?

kassandr · 03-24-2026, 05:46 AM

The hallucination problem discussed here is real, and "say I don't know" helps, but it's a patch on a structural issue. When calibre's Discuss sends a single book to an LLM, the model can only work with what it receives in that session. It has no memory of your library, no access to your annotations, no way to cross-reference between titles. For the question "what does this book say about X?" that's often enough. But for "what do my books say about X?" - across hundreds or thousands of volumes - it's a fundamentally different problem. That's where Retrieval-Augmented Generation (RAG) comes in: instead of asking the model to recall from training data, you index your actual library and feed the relevant passages to the model before it answers. The model responds based on your sources, with citations.

I've been building an open-source tool called ARCHILLES that does exactly this. It connects to Calibre (among others), indexes full text, metadata, and annotations via multilingual embeddings, and exposes the library to any AI model via MCP (Model Context Protocol), so it works with Claude, ChatGPT, local models, whatever you prefer. Everything runs locally, no data leaves your machine. It's MIT-licensed and on GitHub: https://github.com/kasssandr/archilles. Still early, but the core search and citation pipeline is functional.

03-24-2026, 05:46 AM	#19
kassandr Junior Member Posts: 2 Karma: 10 Join Date: Dec 2025 Device: Calibre	RAG solves this at the architecture level, not the prompt level The hallucination problem discussed here is real, and "say I don't know" helps, but it's a patch on a structural issue. When calibre's Discuss sends a single book to an LLM, the model can only work with what it receives in that session. It has no memory of your library, no access to your annotations, no way to cross-reference between titles. For the question "what does this book say about X?" that's often enough. But for "what do my books say about X?" - across hundreds or thousands of volumes - it's a fundamentally different problem. That's where Retrieval-Augmented Generation (RAG) comes in: instead of asking the model to recall from training data, you index your actual library and feed the relevant passages to the model before it answers. The model responds based on your sources, with citations. I've been building an open-source tool called ARCHILLES that does exactly this. It connects to Calibre (among others), indexes full text, metadata, and annotations via multilingual embeddings, and exposes the library to any AI model via MCP (Model Context Protocol), so it works with Claude, ChatGPT, local models, whatever you prefer. Everything runs locally, no data leaves your machine. It's MIT-licensed and on GitHub: https://github.com/kasssandr/archilles. Still early, but the core search and citation pipeline is functional.