🚀 Building an LLM Self-Improvement Engine — Need Feedback
Hey everyone,
I’m currently working on a system (early-stage) that aims to make LLMs continuously improve themselves after deployment.
The idea is simple but powerful:
→ Detect weak areas in model performance
→ Generate targeted synthetic data for those gaps
→ Fine-tune the model iteratively
→ Repeat the loop to create a self-evolving system
Kind of like giving LLMs a feedback + learning loop instead of static training.
💡 Use case I’m targeting:
- Improving domain-specific models without massive manual datasets
- Reducing hallucinations in critical workflows
- Making models adapt faster to real-world usage
⚙️ Rough flow: Evaluation → Weakness Detection → Synthetic Data Generation → Fine-tuning → Re-evaluation
I’d love to get feedback on:
- Does this approach already exist in a strong form?
- What are the biggest technical challenges you see here?
- Any tools/frameworks you’d recommend for building this efficiently?
Appreciate any thoughts, criticism, or ideas 🙌
— Building in public