Integrating generative AI is no longer experimental. Products like DetectGPT, Archi AI, and AppGen prove that AI can be the core value proposition of successful commercial apps.
Identify the right use case
Generative AI works best for tasks where output is text, image, or code, and where users tolerate some variability. Content automation, assistants, document analysis, and design generation are strong cases.
Avoid AI where 100% deterministic accuracy is required without human review.
Recommended architecture
Never call AI APIs directly from the frontend. Use a backend layer that manages prompts, rate limiting, caching, and logging. Consider RAG (Retrieval Augmented Generation) when you need business-specific context.
For SaaS products, implement per-user quotas and monitor costs per request from day one.
Practical stack
OpenAI and Anthropic cover most use cases. Combine with embeddings (text-embedding-3-small) and a vector database like Pinecone or pgvector for RAG.
Next.js API routes or Node.js microservices work well as an orchestration layer.