Skip to main content

31. AI-Enhanced Tarot Interpretations

Status: Accepted Date: 2025-07-06

Context​

A standard tarot reading application provides static, pre-written interpretations for each card. This can feel impersonal and may not resonate with the user's specific query or situation. To provide a more dynamic and personalized experience, we can leverage Large Language Models (LLMs) to generate contextualized interpretations.

Decision​

We will use an AI service (powered by a local Ollama instance for privacy) to generate enhanced, contextualized interpretations for tarot readings. When a user performs a reading, the application will send the user's query, the cards drawn, and their positions to the AI service. The AI, guided by a sophisticated system prompt and a user-selected persona, will generate a unique interpretation that weaves the traditional card meanings into a narrative that addresses the user's specific question.

Consequences​

Positive:

  • Personalized Experience: The interpretations are tailored to the user's specific query and context, making the reading feel more personal and relevant.
  • Dynamic & Unique Content: Every reading is unique, providing high replay value.
  • Deeper Insights: The AI can draw connections between cards and the user's situation in ways that static interpretations cannot, potentially offering deeper insights.

Negative:

  • Interpretation Quality: The AI may sometimes generate interpretations that are generic, nonsensical, or deviate too far from traditional tarot symbolism.
  • Latency: Calling an AI service introduces latency, which can make the reading process slower than simply retrieving static text.
  • Cost & Complexity: Running and maintaining an AI service (even a local one) adds complexity and resource costs to the application's infrastructure.

Mitigation:

  • High-Quality System Prompts: Engineer detailed, high-quality system prompts that provide the AI with strong guidance on tarot symbolism, interpretation structure, and the desired tone for each persona.
  • User-Configurable Personas: Allow users to choose from different "personas" (e.g., "Sage," "Oracle," "Therapist"), which use different system prompts to alter the style of the interpretation. This gives users more control over the output.
  • Regeneration & Feedback: Provide a mechanism for users to regenerate an interpretation if they are unsatisfied with the first one. Optionally, include a feedback mechanism (e.g., thumbs up/down) to collect data for improving the prompts over time.
  • Local LLMs: Use locally-hosted LLMs (via Ollama) to ensure user privacy and control costs.