AI Features Are Not Magic
Adding "AI" to your product buzzword strategy is easy. Adding AI features that are genuinely useful, fast, and cost-effective is hard. We've done both.
Here's what we've learned building AI-powered features across multiple production applications.
Choose the Right Model for the Task
Not every task needs the most powerful model:
| Task | Model Choice | Why |
|---|---|---|
| Content generation | Claude Sonnet | Quality + speed balance |
| Code generation | Claude Sonnet | Strong reasoning |
| Classification/tagging | claude-haiku-4-5 | Fast, cheap, accurate enough |
| Document analysis | Claude Opus | When quality is critical |
Most production features should use a mid-tier model. The cost difference is substantial at scale.
Prompt Engineering Is Real Engineering
The quality of your AI feature is largely determined by your prompts. We treat prompts as code:
- Version-controlled in the repository
- Tested with representative inputs
- Reviewed before deployment
- Monitored for output quality
const BLOG_PROMPT = `You are a content writer for Spent Digital Labs,
a software studio in Nigeria. Write a blog post about {topic}.
Requirements:
- Tone: Professional but approachable
- Length: 800-1200 words
- Include: practical examples, code snippets where relevant
- Audience: Technical founders and developers
- Format: Markdown with clear headings`;
Handle Failure Gracefully
LLMs fail. They time out. They produce malformed output. Your application must handle this:
- Timeouts with retry logic
- Output validation before storage
- Graceful degradation if AI is unavailable
- User feedback when generation fails
Cost Management
Stream responses instead of waiting for complete generation — better UX and earlier time-to-first-token. Cache deterministic outputs. Use webhooks for async generation tasks.
AI features can be expensive at scale. Design for cost from the start.
