Adding AI to a SaaS product is easy to announce and hard to operate. The difference is whether the feature becomes a durable product surface or a demo that quietly dies after the launch week.
This is the approach we use when integrating LLM capabilities into real products — including storytelling, coaching, and assistant-style features.
1. Start with a job-to-be-done, not a model
Write one sentence:
For [user], when [situation], the product should [outcome], so they can [value].
Examples:
- For a parent, when preparing bedtime, generate a short story from a few keywords
- For a subscriber, when stuck on a habit, ask coaching questions that move them forward
If you cannot write the sentence, you are not ready to pick a model.
2. Prefer thin vertical slices
Ship the smallest path that touches:
- UI entry point
- Auth and entitlement check
- Prompt/input assembly
- Model call
- Persistence of result
- Failure and retry UX
Do not begin with a platform rewrite.
3. Put the model behind your backend
Mobile and browser clients should not own provider keys or prompt policy.
A durable pattern:
- Client sends structured intent
- Backend validates plan limits and abuse controls
- Backend builds prompt / tool context
- Worker or request path calls the model
- Backend stores result + metadata (model, tokens, latency, safety flags)
This is how you keep cost, safety, and observability under your control.
4. Design for partial failure
LLM features fail in distinctive ways:
- timeouts
- empty or low-quality output
- policy refusals
- provider outages
- unexpectedly expensive prompts
Your UX needs:
- clear pending states
- retry without duplicate charges where possible
- graceful fallback copy
- operator visibility into failure rates
5. Control cost like a product feature
Treat budget as part of the design:
- per-user / per-plan quotas
- max input size
- caching for repeated asks
- cheaper models for drafts, stronger models for finalization when needed
- queueing during spikes
If cost is unbounded, the feature is not production-ready.
6. Keep human-readable audit trails
Store enough metadata to answer:
- What did the user ask?
- Which model version ran?
- How long did it take?
- Was output edited before publish?
- Did safety filters trigger?
You do not need to log secrets. You do need forensic clarity.
7. Separate “assistant UX” from “system of record”
AI can draft, suggest, summarize, and coach. Your database remains the source of truth for entitlements, content, and user data.
Never let a probabilistic model become the only place critical business state exists.
A sensible rollout order
- Internal dogfood on a limited cohort
- Paid plan beta with quotas
- Expand surfaces after quality and cost stabilize
- Only then market the feature as core product value
Bottom line
Successful AI features are product engineering problems: UX, backend boundaries, quotas, evaluation, and operations. The model is one component — not the architecture.