Build a Startup Recognition System MVP AI Prompt
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Turn scattered docs into instant, trustworthy answers. Automate ingestion, search, and citations so your team and customers get help fastβwithout digging through folders or chats.
A typical RAG workflow pulls knowledge from places you already store it (Google Drive, Notion, docs, PDFs, web pages). It then cleans and splits the text into searchable chunks and saves them in a vector database like Pinecone. When someone asks a question, the workflow retrieves the most relevant chunks and sends them to an AI model (like OpenAI) to draft an answer. You get faster replies plus citations, so teams trust the output and customers get consistent information.
Not necessarily. If you can connect accounts and follow step-by-step prompts, you can run most workflows as-is. Youβll get the most value by knowing where your βsource of truthβ lives and what should be included or excluded. For more advanced setups (permissions, custom chunking, multiple data sources), you might want help from an ops lead or a freelancerβbut itβs optional, not required.
If your team answers the same questions every day, this can cut that work in half. Support teams spend less time searching and rewriting replies. Marketing stops re-explaining positioning, pricing, and product details in Slack threads. Ops teams donβt have to chase the latest SOP version. Many businesses reclaim about 2 hours per week per person once the knowledge base is kept in sync and answers are generated with reliable sources.
Youβll need three things: a content source (like Notion or Google Drive), an AI model connection (OpenAI or similar), and a place to store embeddings (often Pinecone). Decide who can access which documents, because permissions matter. Start small with one folder or one workspace, then expand once results look good. Frankly, the biggest blocker is messy docsβclean up your βfinalβ versions first and everything works better.
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