Adding an AI feature to a product takes an afternoon. Adding one you can trust in production takes a bit more thought.
Most early SaaS teams do the same thing: paste a prompt into an API call, ship it, and then discover in week two that the output sometimes breaks the UI, costs more than expected, and nobody can explain why one customer got a weird answer.
Here's the checklist I'd follow instead. It's provider-agnostic, so swap in whatever model API you use.
1. Pick one job
Not "add AI." One job. "Extract vendor, total and due date from an uploaded invoice." "Draft a reply to a support ticket." "Summarize a call into action items."
If you can't write the job as a single sentence with a clear input and output, it's too fuzzy to test. And if you can't test it, you can't ship it responsibly.
2. Wrap the model call in your own function
Don't scatter raw API calls across your codebase. Put one thin layer around the model so you can add validation, retries, logging and cost limits in one place.
import json
import time
def call_model(prompt: str) -> str:
"""Swap this for your provider's SDK call."""
raise NotImplementedError
REQUIRED = {"vendor", "total", "due_date"}
def extract_invoice_fields(text: str, retries: int = 2) -> dict:
prompt = (
"Extract vendor, total and due_date from this invoice. "
"Reply with JSON only.\n\n" + text
)
for attempt in range(retries + 1):
try:
raw = call_model(prompt)
data = json.loads(raw)
if not REQUIRED <= data.keys():
raise ValueError("missing fields")
return data
except (json.JSONDecodeError, ValueError):
time.sleep(2 ** attempt) # simple backoff
# Don't fail silently: hand it to a human.
return {"needs_review": True, "raw_text": text}
Three small habits in there are worth copying. The output is validated, not trusted. Failures retry with backoff. And the last line is a fallback to a person instead of a crash or, worse, a confident wrong answer.
3. Treat output as untrusted input
A model can return malformed JSON, an empty string, extra commentary, or a plausible but wrong value. So validate against a schema, check types and ranges, and never render raw model output as HTML.
If the feature writes to your database or triggers actions, add a confirmation step for anything irreversible. "The model said so" is not an audit trail.
4. Log what you'd need to debug it
When a user says "the AI got it wrong," you'll want to know what happened. Log the input (or a reference to it), the prompt version, the model name, the raw output, and the latency. Be careful about personal data here: redact or hash what you can, and set a retention limit.
Version your prompts like code. A one-word prompt tweak can change behavior, and you'll want to trace which version produced which result.
5. Put a ceiling on cost and latency
Set timeouts. Set a max input size. Set per-user or per-account rate limits. And track cost per request from day one, because AI features often cost more per use than the rest of your stack put together.
A quick sanity check: multiply your expected daily requests by the cost per request. If that number makes you wince, fix it before launch, not after the first invoice.
6. Keep a human in the loop at first
For the first few weeks, route a sample of outputs (or all of them, if the stakes are high) to a review queue. Track how often reviewers change the result. That correction rate is your real quality metric, and it tells you when it's safe to automate more.
7. Build a tiny evaluation set
Collect 30 to 50 real examples with correct answers. Run them every time you change the prompt or the model. It isn't fancy, but it catches regressions that "it looked fine when I tried it" never will.
What to skip in the MVP
You probably don't need custom model training, a vector database or an agent framework on day one. Most MVPs need a good prompt, solid validation and a way for humans to correct mistakes. Add complexity when the data shows you need it.
Quick checklist
- [ ] One clearly defined job
- [ ] Model call wrapped in one function
- [ ] Output validated against a schema
- [ ] Retries, timeouts and a human fallback
- [ ] Logging with prompt version and model name
- [ ] Cost and rate limits
- [ ] A small evaluation set
Where to go from here
If you're planning the rest of the product around this feature, this step-by-step guide to building a SaaS MVP covers scoping, feature choice and launch. And if you'd rather have a team build it with you, Diginatives offers MVP development services for exactly this stage.
What's the strangest failure you've seen from an AI feature in production? Drop it in the comments.
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