PropensityAI Documentation

PropensityAI Docs

Welcome to the official PropensityAI documentation. This page explains how to use PropensityAI features in a practical, user-focused way so you can get better answers, faster research outcomes, and a more personalized search experience.

What is PropensityAI?

PropensityAI is an AI-powered search experience designed to help you discover, understand, and validate information. Instead of just returning links, it provides structured answers that you can refine with follow-up questions and source review.

How to use PropensityAI effectively

Start with a clear question: ask in natural language and include context when relevant.
Define your goal: mention whether you want a summary, comparison, explanation, or step-by-step guidance.
Check source-backed output: review source references for confidence and verification.
Refine with follow-ups: ask focused follow-up questions to improve precision.
Iterate quickly: small query improvements often lead to significantly better results.

Core feature guide

Web search answers: receive concise answers supported by source references.
File analysis: upload images, documents, audio, or video and ask targeted questions about the content.
Follow-up suggestions: discover related next questions to deepen research.
Voice input: ask questions by speaking directly into the search interface.
Personalization tools: use Memory, Lessons Learned, and Source Reranking for a tailored experience.

Memory settings: definition and purpose

Memory in PropensityAI means your experience can adapt over time based on relevant preferences and interactions. When enabled, Memory helps make future answers more aligned with your style and recurring needs.

Use Memory when you want consistent answer style and better continuity across sessions.
Disable Memory when you prefer a clean, neutral, session-by-session experience.
Memory is most useful for repeated workflows such as research, learning plans, and recurring analysis tasks.

Lessons Learned: what it means

Lessons Learned are short, practical improvements captured from your interactions over time. In simple terms, this helps PropensityAI remember what tends to work better for you and apply that guidance when relevant.

Useful for users who frequently refine answers and want that feedback to improve future outputs.
Helps maintain continuity in response quality across related questions.
Works best when your requests are specific and goal-oriented.

Source Reranking: definition and benefits

Source Reranking means adjusting how source results are prioritized so the platform can better reflect your trusted source preferences. Over time, this can help surface the kinds of sources you find most useful.

Great for users who repeatedly evaluate source quality by domain, depth, and clarity.
Improves discoverability of preferred sources for future searches.
Supports faster decision-making when source trust matters.

Tips for better results

Ask one focused question at a time to reduce ambiguity.
For comparisons, define criteria such as price, features, pros/cons, or timeline.
For local intent, add location context (city or region) when needed.
For uploads, use explicit prompts like “summarize this document” or “explain this chart.”
Use follow-up prompts strategically to narrow, verify, or expand the answer scope.

When PropensityAI works best

PropensityAI is ideal for research, learning, fast information discovery, source-based validation, and multi-step exploration. It is especially useful when you need both speed and confidence in what you read.

Docs FAQ

What is the difference between Memory and Lessons Learned? Memory is the broader personalization mode; Lessons Learned are practical improvements that contribute to better future responses.
Why review sources? Source-backed answers improve trust, transparency, and verification.
Do I need follow-up questions? Not always, but follow-ups are the fastest way to make answers more precise.