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Uncategorized

31
Jan
Vector database vs managed RAG platform
What Is the Difference Between Using a Vector Database vs. A Managed Rag Platform?

A Vector Database stores and retrieves high-dimensional embeddings for semantic search, while a Managed Retrieval-Augmented Generation (RAG) Platform combines vector search with AI-powered answer generation, data management, and user-friendly features to deliver ready-to-use AI solutions. What does a Vector Database […]

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29
Jan
AI Q&A bot on course pages
How Do I Integrate an AI Q&a Bot Into My Kajabi or Teachable Course Pages?

You can integrate an AI Q&A bot into Kajabi or Teachable by embedding an AI widget or iframe into your course pages and connecting it to your course content. Platforms like CustomGPT allow you to train the AI on your […]

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29
Jan
Hybrid search keyword plus vector
How Do I Implement Hybrid Search (Keyword + Vector) for Better Accuracy?

Hybrid search combines traditional keyword-based retrieval with vector-based semantic search to improve result accuracy, relevance, and user satisfaction by leveraging the strengths of both methods. In practice, hybrid search allows systems to match exact terms while also understanding the intent […]

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29
Jan
Chatbot trained on support emails
How Do I Train a Customer Service Chatbot Using My Past Email Support Transcripts?

You train a customer service chatbot using past email support transcripts by securely uploading and processing your historical email data into a knowledge-based AI system. This enables the chatbot to learn common questions, phrasing, and resolutions from real conversations, improving […]

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29
Jan
Support bot trained on documentation
How Do I Train a Support Bot Using My Documentation Only?

You train a support bot using your documentation only by using a knowledge-based AI system that ingests your help docs, policies, and manuals and restricts answers strictly to those sources. This prevents guessing, improves accuracy, and ensures the bot delivers […]

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29
Jan
Troubleshooting context window limits
How Do I Troubleshoot Context-Window Limits When Dealing With Large Documents?

Context-window limits restrict how much text an AI model can process in a single input. To handle large documents, you must strategically chunk content, use summarization, and manage input size to ensure relevant context fits within limits, improving AI understanding […]

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29
Jan
Chunking large PDFs for RAG
How Do I Handle Chunking Strategies for Large PDF Documents in Rag Systems?

Chunking large PDF documents into manageable, meaningful sections is critical for effective Retrieval-Augmented Generation (RAG) systems. Proper chunking improves retrieval accuracy, reduces noise, and enhances AI answer relevance by breaking content into semantic units sized for optimal embedding and search. […]

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26
Jan
AI Categorize Support Ticket Tags
How Can I Use AI to Categorize and Tag Incoming Support Questions Automatically?

AI can automatically categorize and tag incoming support questions by analyzing message intent, keywords, and context, then mapping each request to predefined topics, priorities, and workflows. This reduces manual triage, speeds up response times, and improves reporting accuracy. In practice, […]

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26
Jan
Illustration of an AI chatbot updating answers when a new help article is published.
Can I Build an AI Chatbot That Automatically Updates When I Publish a New Help Article?

Yes. You can build an AI chatbot that automatically updates when you publish new help articles by connecting it directly to your help center, knowledge base, or documentation source. CustomGPT does $this by syncing with your content so answers stay […]

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26
Jan
Searchable archive of meeting transcripts
How Do I Build a Searchable Archive of Company Meeting Transcripts Using AI?

Building a searchable archive of company meeting transcripts with AI involves transcribing audio/video into text, cleaning and structuring that text, indexing it with semantic search, and deploying AI-powered tools that allow employees to query and retrieve relevant meeting insights quickly. […]

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