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Ramayana meets AI is a website that combines English translations of the Ramayana with generative AI capabilities. It allows users to ask questions about the Ramayana and receive AI-powered answers, simulating a conversation with the sages of yore. The platform uses award-winning English translations of Valmiki and Tulsidas' Ramayan and trains Google's PaLM AI to generate responses.
Users can enter questions about the Ramayana into the search box to receive AI-generated answers. The platform provides concise responses and links to the relevant sections of the source text.
Please email the text of the Ramayana document you wish to add to hello@vineets.com. Note that only text documents are accepted, not images.
Your questions may be too long or complex. Try asking simpler or more concise questions.
The generative AI is optimized for quick responses. You can find the referenced sections below the response box, and you may also see citation links in superscript next to the answers.
You can support the project by sharing it with friends, family, and online communities where others might find it interesting.
Free plan available.
Use these comparison pages to understand the trade-offs between the models most relevant to Ramayana meets AI.
Compare Gemini 1.0 Pro Deprecated and Gemini 1.5 Flash Vision Deprecated across pricing, context window, capabilities, benchmarks, and API access to choose the better fit for long-context workloads versus general-purpose AI workloads.
Compare Gemini 1.5 Pro Vision Deprecated and Gemini 1.0 Pro Deprecated across pricing, context window, capabilities, benchmarks, and API access to choose the better fit for general-purpose AI workloads versus long-context workloads.
Compare Gemini 1.0 Pro Vision Deprecated and Gemini 1.5 Flash Vision Deprecated across pricing, context window, capabilities, benchmarks, and API access to choose the better fit for general-purpose AI workloads versus general-purpose AI workloads.
Compare Gemini 1.5 Pro Vision Deprecated and Gemini 1.0 Pro Vision Deprecated across pricing, context window, capabilities, benchmarks, and API access to choose the better fit for general-purpose AI workloads versus general-purpose AI workloads.
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