Hi Google AI community,
I have been exploring open AI models and the different approaches developers use when moving AI applications from prototypes into production environments.
While open models provide flexibility, production systems often introduce additional challenges such as:
- Model performance optimization
- Context management and retrieval pipelines
- Fine-tuning vs prompt engineering decisions
- Deployment and infrastructure considerations
- Monitoring model quality over time
I would like to learn from developers working with Gemma and other open models:
- What challenges have you faced when deploying open models in real-world applications?
- When do you prefer fine-tuning a model versus using RAG-based approaches?
- What tools or workflows are helping you evaluate model performance?
Would love to hear practical experiences and recommendations from the community.
Hi @codeautomation
Based on my experience of working with Gemma models, Performance is usually the first one. A model that works well locally can behave very differently with concurrent requests, longer contexts, and memory constraints. Quantization and choosing the right model size can help a lot, but I prefer benchmarking against the actual workload rather than relying only on model benchmarks.
For RAG vs fine-tuning, if the model needs access to changing or private information, start with RAG. If it already knows the information but needs to consistently follow a specific format, style, or behavior, fine-tuning can make more sense. They can also work together.
For evaluation, even a small dataset of real application queries is extremely useful. It gives you something consistent to measure when changing the model, prompts, RAG setup, or inference settings.
Hope this helps