AI development has become much more accessible with LLM APIs, RAG frameworks, AI agents, and other development tools. Building an AI prototype can often be done quickly, but taking it into production seems to involve a very different set of challenges.
For developers and teams working on AI development, what have you found to be the biggest challenges?
Some areas I'm particularly interested in are:
Choosing the right AI model for a specific use case
Improving accuracy and reducing hallucinations
Implementing RAG effectively
Managing AI development and infrastructure costs
Integrating AI with existing applications and databases
Handling security and data privacy
Monitoring AI application performance
Managing context and conversation history
Scaling AI workloads
Deciding between using an existing AI API and developing a custom model
I'm also curious about architecture decisions. When adding AI capabilities to an existing application, is it generally better to build AI as a separate service or integrate it directly into the existing backend?
For those who have taken an AI project from prototype to production, what challenges did you encounter that you didn't anticipate during the initial AI development phase?
I'd be interested to hear what approaches, tools, or practices have worked best for you.