Essay
When not to use AI
Five situations where a regular form, filter, or rule engine beats an LLM call.
Not every problem needs an LLM. Here are five situations where we'd push back on an "AI feature" request.
1. Deterministic lookups
If the answer is in your database, query the database. Don't embed documents and hope RAG finds the right row.
2. Simple classification with clear rules
"If subject contains X, route to team Y" doesn't need a model. Regex and rules are faster, cheaper, and debuggable.
3. Format conversion
JSON → CSV, markdown → HTML: use a library, not GPT.
4. Features users don't want
A chat sidebar nobody asked for isn't fixed by a better model. Kill the feature.
5. When latency budget is under 200ms
LLM calls are seconds. If the UX requires instant feedback, AI isn't the tool.
Related
- 01
Building a Cost-Efficient AI-Powered SaaS MVP: Next.js 16 Edge Functions with Local Model Inference
Slash AI API costs for your SaaS MVP. Learn to leverage Next.js Edge Functions with local model inference (Ollama, Transformers.js) for faster, cheaper, and more private AI features.
Guide · 9 min read - 02
Shipping AI features that don't feel grafted on
The difference between a useful AI feature and an afterthought comes down to five design decisions.
Essay · 6 min read
Want this built for you?
Fixed-scope MVP sprints and AI integrations. Same stack as this guide.