The gap between AI demos and production AI is enormous. Here's how to bridge it without burning your budget on science projects.
Start With a Real Problem
Don't adopt AI because it's trending. Identify a specific workflow that's slow, error-prone, or impossible to scale with humans alone. Document processing, customer support triage, and content generation are proven starting points.
The Provider Abstraction
Lock-in is real. OpenAI is dominant today, but Claude, Gemini, and open-source models are catching up fast. Build a thin abstraction layer from day one:
export interface AiProvider {
chat(prompt: string, context?: string): Promise<string>;
}
This costs almost nothing to implement and saves you weeks when you need to switch or add providers.
RAG Over Fine-Tuning
For most business applications, Retrieval-Augmented Generation (RAG) beats fine-tuning. Feed your documents into a vector store, retrieve relevant context at query time, and let the model synthesize an answer. It's cheaper, more maintainable, and easier to keep current.
Key Takeaways
- Solve a real problem, not a hypothetical one
- Abstract your AI provider from day one
- RAG is usually the right choice for business data
- Ship a minimal version fast, then iterate based on real usage