Build a knowledge base chatbot with Jotform, RAG Supabase, Together AI & Gemini

A RAG (Retrieval-Augmented Generation) chatbot combines document retrieval with AI generation. It searches your knowledge base for relevant information, then uses AI to generate natural-sounding answers. This provides accurate responses while maintaining conversational flow.

Businesses use them for 24/7 customer support, employee training, and reducing repetitive inquiries to human teams. The system improves over time as you add more documents and refine the knowledge structure.

• More accurate than pure generative AI
• References your actual documentation
• Reduces hallucination risks

Jotform excels at collecting structured data from customers. When integrated with a RAG system, form submissions can automatically update your knowledge base. For example, product feedback forms can feed into FAQ improvements.

This creates a self-improving system where customer interactions continuously enhance your AI's knowledge. You can also use Jotform to collect user feedback on answer quality, creating a closed-loop improvement system.

• Capture customer questions in structured format
• Easy to customize for different query types
• Built-in analytics on question patterns

Supabase provides the vector database needed for RAG systems. It stores document embeddings that allow semantic search across your knowledge base. Unlike keyword search, this understands the meaning behind queries.

Supabase's scalability makes it ideal for growing knowledge bases while keeping search performance fast. The open-source nature also means no vendor lock-in for your critical knowledge assets.

• Stores document vectors for semantic search
• Scalable performance for large knowledge bases
• Self-hostable option available

Together AI offers open-source models you can fine-tune for domain-specific knowledge, while Gemini provides ready-to-use enterprise-grade AI. Use Together AI when you need custom model behavior or data privacy.

Choose Gemini for general knowledge when you prioritize response quality over customization. Many systems use both - Gemini for general queries, Together AI for specialized answers requiring deep domain expertise.

• Together AI for specialized knowledge
• Gemini for general questions
• Combine both for comprehensive coverage

Well-structured documents like FAQs, product manuals, and policy guides yield best results. Break content into logical sections with clear headings. Avoid long paragraphs - chunk information into 200-300 word segments.

PDFs, Markdown files, and structured HTML work better than raw text. Include examples and troubleshooting scenarios where possible. The more concrete your documentation, the better the AI can apply it to real questions.

• FAQs and how-to guides
• Product specifications
• Troubleshooting procedures

Modern RAG systems achieve 85-95% accuracy for well-documented topics. Accuracy depends on your knowledge base quality and coverage. Implement feedback loops where users can flag incorrect answers.

Monitor conversations to identify knowledge gaps. The system improves over time as you expand coverage for common queries it struggles with. Start with your most frequent questions and gradually expand scope.

• 85-95% accuracy for covered topics
• Improves with knowledge base expansion
• User feedback enhances performance

Yes! GrowwStacks specializes in tailored AI automation solutions. Our team can build a custom chatbot integrated with your specific tools and knowledge sources. We handle everything from document processing pipelines to conversation design.

We'll analyze your most common support queries, structure your knowledge base for optimal AI performance, and integrate with your existing systems. The result is a solution perfectly adapted to your business needs and customer expectations.

• Custom-trained for your industry
• Integrated with your existing tools
• Ongoing optimization included