AI Knowledge Base for AI Document Analysis

RAG operates in three stages. First, your documents convert into numerical representations (embeddings) stored in a vector database. When you submit a question, the system runs a semantic search to find relevant content from your knowledge base. Then this retrieved information passes to the AI model alongside your question. The model generates responses grounded in your actual documents rather than its training data. 

AWS Amazon site declare that RAG "extends the already powerful capabilities of LLMs to specific domains or an organization's internal knowledge base, all without the need to retrain the model."

1. Go to AI Studio
2. Knowledge Base (RAG)
3. Create New Knowledge Base
4. Upload files 
5. Save.

Processing takes 1-3 minutes depending on file size.

Then open any chat, click the Knowledge Base icon, select your database, start asking questions.

You can select multiple knowledge bases simultaneously and combine them with web search.

Eliminating AI hallucinations by grounding responses in your actual documents. Standard LLMs may fabricate information when they lack knowledge. RAG systems retrieve real content from your knowledge base before generating answers. 

Additional advantages according to industry analysis: real-time information access without retraining, source citations for verification, cost-effective updates (refresh documents instead of retraining models).

RAG retrieves external information at query time without modifying the model. Flexible, cost-effective, easy to update - just change your documents.

Fine-tuning permanently modifies model parameters through retraining on specific data. Expensive, time-consuming, requires retraining for updates.

According to comparative analysis: RAG suits dynamic, fact-based applications; fine-tuning works better for specialized tone or style requirements. Many organizations combine both - fine-tuning for behavior, RAG for factual grounding.

Accuracy depends on document quality. Source material reports minimum accuracy around 80%, typical accuracy around 96%, with major hallucinations virtually eliminated. 

Specialized prompts requesting citations from your knowledge base can improve accuracy further.

Yes. Upload audio files directly to the knowledge base. Alternatively, use Cabina.AI Transcriber to convert audio/video to text first, then upload the transcription. [Note: For transcription tasks, the Cabina.AI Transcriber model performs better than standard chat models.]

Individual files should stay under 50-100 MB for optimal processing. File count has no limit - upload as many as needed.

Yes. Select both a Role and a Knowledge Base - they function together. Add prompts from the Prompt Library for enhanced results.

Yes. Start with GPT, continue with Claude, switch to Mistral or Gemini. Context and knowledge base remain active throughout the conversation.

Yes. Click the Search icon alongside your Knowledge Base selection before sending your query. Web search results combine with knowledge base data for comprehensive answers.

Yes. Use Compare Mode: select your knowledge base(s), write one prompt, click send. You receive parallel responses from two different AI models side by side. Continue in compare mode or split into separate chats.

Go to Settings → Billing → Usage. Click any day to view models used and token consumption. RAG queries remain affordable, the technology runs cheaply.

Yes. Enable chat sharing. Your colleague sees AI responses (noting they come from your knowledge base) but cannot access the actual files. Revoke access anytime by toggling Public Mode off.

For small files, attach them via the paperclip icon in your chat with prompt. The system includes them in analysis without waiting for knowledge base regeneration. Useful for adding recent data quickly.

Yes. Download files, edit them, add new ones to existing knowledge bases at any time.