Many LLM and Generative AI projects perform well during the prototype stage but become much harder to manage once they move toward production.
Some of the challenges I frequently see include:
- Maintaining response quality as user queries become more diverse
- Reducing hallucinations in RAG and conversational AI systems
- Choosing between prompt engineering, fine-tuning, and retrieval
- Evaluating LLM responses beyond basic accuracy metrics
- Handling latency and inference costs at scale
- Integrating NLP pipelines with LLM-based applications
- Maintaining conversation context without sending excessive tokens
- Monitoring model performance after deployment
I’m interested in learning how others in the Hugging Face community approach this transition.
For production LLM or conversational AI applications:
What has been your biggest challenge moving from a working prototype to a reliable production system?
Also, which approach has worked best for you: fine-tuning, RAG, prompt optimization, smaller specialized models, or a combination of these?
Would be great to hear practical experiences, architectures, evaluation methods, or Hugging Face tools/models that have worked well.