Perplexity AI collections explained with examples

Perplexity search engine once offered collections, a powerful feature that allowed users to organize related threads. These collections streamlined AI-powered web search by grouping searches and providing consistent instructions for personalized responses. However, Collections have been renamed to Perplexity Spaces, an enhanced version that offers more flexibility, including the ability to upload reference files.

See Also: How To Use Perplexity AI-Powered Search For Beginners

What are Perplexity Collections?

Perplexity Collections were designed to group related threads, making it easy to conduct in-depth research or analyze specific topics. Users could create custom AI instructions to guide the AI search engine in delivering more relevant results across all threads within a collection. Unlike traditional AI websites, collections allowed for a more organized and personalized search experience.

What Are the Benefits of Perplexity Collections?

collections in perplexity offered a lot of benefits depending on your use case. Here are some of the top benefits:

1. Organized Answers: Store answers to related questions in one spot, making it easy to find all your topic-specific answers. Traditional search engines like Google don’t offer this convenience.
2. Customized Responses: Users could set unique rules for each collection, ensuring the AI delivered tailored responses every time.
3. Collaboration: Similar to spaces collections allowed collaboration with other users.
4. Consistent Prompts: Collections applied a unified AI prompt across all threads, making it easier to maintain context and avoid rewriting prompts on each thread.

Common Use Cases for Perplexity Collections

Perplexity Collections proved useful in various scenarios, including:

1. Research and Analysis: Ideal for researchers using AI online to gather, analyze, and organize large datasets.
2. Education: Enabled the creation of customized study guides and learning resources.
3. Professional Development: Helped professionals stay current with the latest developments in their field, analyze complex industry trends, and group important information for quick reference and review.

How to Create and Edit Perplexity Collections

To create a collection, users logged into Perplexity, accessed the library tab, and clicked “Add” in the collections section. They could name their collection, add a description, and provide specific AI instructions to guide perplexity responses. Editing a collection was simple, involving a few clicks to update the prompts or adjust privacy settings.

Examples of Perplexity Collections

Here are some collection examples you can modify to suit your specific use case or project. To test these collections, copy the prompt and descriptions, as they provide context to Perplexity AI.

Example-1 – Discover top stories in your niche

• Collection Name – Digital Marketing News
• Prompt – Use sources from the web that are less than 7 days old, and include sources
• Description – This collection allows users to get at least 10 informative news articles on digital marketing from the web. By entering a simple prompt like “Update me,” users receive 10 trending news headlines useful for digital marketers.

Example-2 – Answer in a specific writing style

• Name – AI Shakespeare bot
• Prompt – strictly respond to every query in Shakespearean English
• Description – This collection allows users to get responses in Shakespearean English for every query

Example-3 – Perplexity Doc simplifier

• Name – Perplexity Document explainer and summarizer
• Prompt – explain the key concepts in the uploaded file like you are explaining to a 9th-grade student. strictly keep your answers to two paragraphs
• Description – This collection allows users to upload files and get the key concepts being discussed in the document explained in simple language by just using the prompt “enlighten me”.

Feel free to modify these collections and create your own custom collections.

Limitations of Perplexity Collections

While collections were a powerful feature in perplexity, they did have some limitations:

• AI Adherence: The AI did not always follow the custom prompt defined in the collection.
• Answer Consistency: Rewriting answers using different models or pro search didn’t retain the previous answers as ChatGPT does, making it difficult to compare the current answer with the previously generated one.
• Accuracy: The AI did not always provide accurate answers, especially when dealing with lesser-known research papers.
• Pro Search Behavior: When using pro search in a thread within a collection, the AI sometimes didn’t follow the AI prompt of the collection and it did not consider the description for context.

Conclusion

While Perplexity Collections played a significant role in improving user experience in perplexity, their evolution into Perplexity Spaces offers even greater possibilities. Spaces continue to build on the strengths of collections, making perplexity more dynamic and flexible.