About Prompt
- Prompt Type – Dynamic
- Prompt Platform – ChatGPT, Grok, Deepseek, Gemini, Copilot, Midjourney, Meta AI and more
- Niche – Music Apps
- Language – English
- Category – Entertainment
- Prompt Title – AI Agent Prompt for Personalized Music Recommendation
Prompt Details
**Prompt Type:** Dynamic
**Platform Compatibility:** All AI Platforms
**Purpose:** Entertainment – Personalized Music Recommendation within a Music App
**Description:** This prompt aims to generate highly personalized music recommendations based on dynamic user input and context. It’s designed to be flexible and adaptable to various AI platforms and can be integrated into music app features like playlist generation, radio stations, or “discover” sections. It leverages a multi-faceted approach, considering various factors influencing music preference to deliver the most relevant and enjoyable experience.
**Prompt Structure:**
“`
## Personalized Music Recommendation Request
**User ID:** {user_id} *(Unique identifier for the user)*
**Current Context:** {context} *(e.g., “Relaxing at home,” “Working out,” “Commuting,” “Partying,” “Studying,” “Feeling happy,” “Feeling sad”)*
**Time of Day:** {time_of_day} *(e.g., “Morning,” “Afternoon,” “Evening,” “Night”)*
**Location (Optional):** {location} *(e.g., “Beach,” “Cafe,” “Gym”)*
**Device (Optional):** {device} *(e.g., “Smartphone,” “Smart Speaker,” “Car”)*
**Current Activity (Optional):** {activity} *(e.g., “Cooking,” “Driving,” “Reading”)*
**Weather (Optional):** {weather} *(e.g., “Sunny,” “Rainy,” “Cloudy”)*
**Music Preferences (Dynamically Updated):**
* **Liked Songs:** [{list of song IDs or titles}]
* **Disliked Songs:** [{list of song IDs or titles}]
* **Liked Artists:** [{list of artist IDs or names}]
* **Disliked Artists:** [{list of artist IDs or names}]
* **Liked Genres:** [{list of genres}]
* **Disliked Genres:** [{list of genres}]
* **Liked Moods/Themes:** [{list of moods/themes, e.g., “Energetic,” “Chill,” “Romantic,” “Nostalgic”}]
* **Disliked Moods/Themes:** [{list of moods/themes}]
* **Recently Played Songs:** [{list of song IDs or titles with timestamps}]
* **Average Listening Duration per Song:** {average_duration} *(in seconds)*
* **Skip Frequency:** {skip_frequency} *(e.g., “High,” “Medium,” “Low”)*
* **Explicit Content Preference:** {preference} *(e.g., “Allow,” “Disallow”)*
**Request Type:** {request_type} *(e.g., “Generate Playlist,” “Recommend Next Song,” “Discover New Artists,” “Create Radio Station”)*
**Number of Recommendations (If Applicable):** {number}
**Additional Instructions (Optional):** {instructions} *(e.g., “Focus on female vocals,” “Instrumental only,” “Similar to [song/artist],” “Explore a new genre”)*
**Output Format:** {output_format} *(e.g., “JSON,” “Text list,” “Comma-separated values”)*
“`
**Implementation Notes:**
* **Dynamic Updates:** The “Music Preferences” section should be constantly updated based on user interactions within the app. This ensures the recommendations remain relevant and personalized over time.
* **Contextual Awareness:** Utilize the context information (time, location, activity, weather, etc.) to refine recommendations. For example, suggest energetic music for workouts and calming music for bedtime.
* **Diversity vs. Familiarity:** Balance recommending familiar artists/genres with introducing new music based on user preferences and listening history. Implement an exploration factor to control this balance.
* **AI Platform Adaptation:** Adjust the prompt structure and terminology based on the specific AI platform’s capabilities and requirements. Ensure proper data formatting for input and output.
* **A/B Testing:** Conduct A/B testing with different prompt variations and algorithms to optimize recommendation accuracy and user satisfaction.
* **Feedback Loop:** Integrate a feedback mechanism within the app to allow users to rate or provide feedback on recommendations. This data can be used to further refine the AI model and improve personalization.
* **Cold Start Problem:** For new users with limited listening history, utilize general popularity data, genre-based recommendations, or onboarding questions to gather initial preferences.
* **Privacy Considerations:** Ensure compliance with data privacy regulations and be transparent with users about how their data is used for personalization.
**Example Usage:**
“`
## Personalized Music Recommendation Request
**User ID:** user123
**Current Context:** Relaxing at home
**Time of Day:** Evening
**Music Preferences:**
* **Liked Songs:** [“Song A,” “Song B,” “Song C”]
* **Liked Artists:** [“Artist X,” “Artist Y”]
* **Liked Genres:** [“Jazz,” “Lo-fi”]
* **Request Type:** Generate Playlist
**Number of Recommendations:** 10
**Output Format:** JSON
“`
This prompt provides a comprehensive framework for building a robust and personalized music recommendation system within a music app. Its dynamic nature allows for continuous refinement and adaptation to individual user preferences, leading to a more engaging and enjoyable listening experience.