AURA — Adaptive AI that learns from every interaction

AURA is a feedback-powered Retrieval-Augmented Generation (RAG) pipeline that continuously improves itself. It uses multi-query retrieval, reranking, and automatic preference signals (WildFeedback) to fine-tune lightweight adapters on a quantized large language model. As users engage, AURA becomes smarter and more aligned with their needs — without manual labeling.

What is AURA?

AURA is an adaptive and autonomous system for generating and training LLM responses with RAG capabilities. Its unique WildFeedback mechanism automatically interprets user satisfaction and preferences, creating training datasets without human annotation. Early research shows performance comparable to manual labeling — but faster and at lower cost.

Why it matters

Traditional AI training is expensive and slow because it relies on manual annotation. AURA removes this bottleneck by capturing signals directly from real conversations and continuously updating the model. This makes AI more personalized, efficient, and scalable.

Architecture at a glance

At a high level, AURA combines:

  • Multi-query retriever
  • Context reranker
  • WildFeedback signals
  • Lightweight fine-tuning with adapters

The system forms a loop: retrieve → generate → gather feedback → fine-tune → redeploy. This cycle repeats so AURA continuously improves.

Pipeline

1. Retrieval & Reranking — Relevant context is retrieved and ranked for each query.
2. Generation — A quantized LLM produces responses based on the selected context.
3. WildFeedback — User satisfaction and preferences are automatically captured as feedback signals.
4. Fine-tuning — The best feedback is used to train lightweight adapters via efficient methods.
5. Deployment — Updated adapters are validated and deployed, closing the loop.

Contact Us

Email: zhanmadi@aura-llm.com