Continuous learning: RAG
Empower businesses with self-adapting document extraction that learns from user corrections, ensuring more accurate and customizable data extraction for your document workflows.
What is Retrieval-Augmented Generation (RAG) ?
Retrieval-Augmented Generation (RAG) is an advanced AI technique that enhances the performance of generative models by incorporating relevant information retrieved from a database. Instead of relying solely on pre-trained knowledge, RAG dynamically fetches contextual examples, enriching the input before generating responses.
Mindee’s RAG solution instantly updates extraction rules without retraining downtime, ensuring maximum relevance through hybrid text and layout-based matching. Unlike static OCR, it dynamically adjusts extraction logic and integrates seamlessly into workflows for effortless adoption.
How does RAG work? ⚙️
Retrieval – The model searches a knowledge base to find relevant documents or examples related to the user’s query.
Augmentation – The retrieved information is injected into the model’s input, providing additional context.
Generation – The AI processes the enriched input to produce a more accurate and context-aware response.
Why Use RAG? ⭐️
More accurate outputs – By grounding responses in real-world data, RAG reduces hallucinations and enhances reliability.
Adaptability – The system can incorporate new information without requiring model retraining.
Optimized for business needs – Ideal for document processing, customer support, knowledge management, and more.
1 example is all it takes to correct future extractions for similar documents.
Achieves 99% accuracy on documents enhanced through RAG adaptation.
Models adjust in under 3 minutes for entirely new document types, ensuring rapid deployment.
Key benefits
No need to wait for model retraining. Users can instantly refine extraction on similar documents.
Move beyond rigid template-based approaches and tailor extraction to unique business needs.
Works within Mindee’s Workflow API, allowing effortless adoption across industries.
Combines textual and layout-based analysis to find the best-matching examples in real-time.
Supports 1,000+ learning documents per RAG instance, with easy expansion based on needs.
Core features
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Learning document database
Store and manage curated document examples to fine-tune extraction.
Advanced similarity matching
Matches new documents based on text and layout, ensuring precise adaptation.
Natural language context
Make your model smarter by adding natural language context to your RAG documents.
Granular extraction control
Users can override and refine only the fields they need, preserving efficiency.
Full SDK & webhook support
Ensures easy implementation into existing workflows.
Adaptive model enhancement
Continuously improves extraction accuracy with user corrections and new document inputs over time.
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