Launching an AI feature is different from launching a traditional feature, and the difference bites hardest in the first week after you ship.
With a regular feature, the code does what the code does. With an AI feature, you're releasing something that behaves differently under load, costs more per user than you modeled, and might produce embarrassing outputs on edge cases nobody thought to test. The post-launch retrospective isn't optional — it's where you figure out whether you have a viable feature or an expensive liability.
What Makes AI Launches Different
If you've shipped software before, you already have intuitions about what can go wrong. AI launches share some of those failure modes and add several new ones:
Costs don't scale linearly with users. A traditional feature might add marginal server cost per user. An LLM feature adds token cost per interaction, and users who love the feature use it more, which costs more, which might be great or might be financially unsustainable. You often can't tell which until real users hit it.
Quality changes under real conditions. Your evaluation suite runs clean test cases. Real users send malformed inputs, paste in huge documents, ask for things you didn't anticipate, and try to break things (sometimes on purpose). Quality at scale is always worse than quality in testing.
Rate limits become architecture. When you're calling an external API, your feature's capacity is bounded by someone else's rate limits. If your launch drives more traffic than your rate limit allows, users hit errors that have nothing to do with your code.
The feedback loop is slower than you'd like. With a traditional feature, you can see immediately whether buttons get clicked and forms get submitted. With an AI feature, you need time to assess whether the outputs are actually good — and "good" might mean different things to different users.
Before the Launch: What to Have in Place
This isn't a comprehensive launch checklist — your team knows how to ship software. These are the AI-specific preparations that are easy to overlook:
Cost controls. Set a hard spending cap with your API provider or on your infrastructure. Know what your daily budget is and have alerts set at 50%, 75%, and 90%. If you don't have cost controls in place, a successful launch (lots of users!) can turn into a budget incident.
Quality monitoring for AI outputs. You need something — anything — that tells you whether outputs are good in production, not just in your test suite. This could be user feedback signals (thumbs up/down), automated evaluation on a sample of production outputs, or manual review of a random subset. Define "good enough" before you launch.
A kill switch. You should be able to turn off the AI feature without redeploying. A feature flag, a config change, something. If outputs go haywire or costs spike, you need to stop the bleeding quickly.
Graceful degradation. What happens when the AI is unavailable? Rate limited? Slow? If your answer is "the feature just breaks," fix that before launch.
Baseline metrics. Capture your current state before the AI feature goes live: the metrics you're hoping to improve, the costs you're hoping to justify, the user experience you're hoping to enhance. Without a baseline, your retro will be "this feels like it went okay" instead of "here's what changed."
The Phased Rollout Argument
Rolling out an AI feature to everyone on day one is tempting — you've been working on it for months and you want to see the impact. But phased rollouts are especially valuable for AI features because they let you catch problems when the blast radius is small.
A sensible progression:
- Internal dogfood (1 week): Your team uses it on real work. Not a demo, not a test environment — actual daily use.
- Small cohort (1-2 weeks): 5-10% of users. Enough to see real usage patterns, small enough that problems affect few people.
- Broader rollout (1-2 weeks): 25-50% of users. You're now testing at scale and validating that cost projections hold.
- General availability: Everyone gets it.
At each stage, review quality, cost, and user feedback before expanding. This doesn't need to be a formal meeting at every stage — sometimes a quick Slack check-in with the metrics open is enough. But don't skip the check.
The Post-Launch Retrospective: A Three-Pass Approach
Instead of running one big retro, do three passes at different time scales. Each one catches different things.
Pass 1: Day-One Review (30 minutes, next business day)
This is a quick sync focused on immediate surprises. Don't overanalyze — you don't have enough data yet.
What to discuss:
- Did anything break or behave unexpectedly?
- Are costs tracking to our projections, or are there surprises?
- Any user reports that need immediate attention?
- Is the monitoring giving us useful signals, or do we have blind spots?
Output: A short list of urgent fixes, if any. Most day-one findings should be "we'll watch this" rather than "we need to change something."
Pass 2: Week-One Deep Dive (60 minutes, end of first week)
Now you have real data. This is where the substantive discussion happens.
Data to prepare:
- Daily active usage and usage patterns (when, how much, what types of requests)
- Actual cost vs. projected cost, broken down by usage pattern
- Quality signals: user ratings, edit rates, error rates, any manual review results
- Performance data: latency distribution, timeout rates, rate limit hits
- Support tickets and user feedback related to the AI feature
Discussion structure:
What surprised us? Start here. The gap between expectations and reality is where the most useful insights live. Maybe usage was 3x what you projected. Maybe users are using the feature for something you didn't design it for. Maybe the quality is better than expected in some areas and worse in others.
What should we change in the next week? This is about tactical adjustments. Prompt tweaks, caching strategies, UX changes to guide users toward better inputs, cost optimization for obvious waste.
What needs more data before we can decide? Some things will be unclear after one week. Name them explicitly and decide what data you need and when you'll have enough.
Pass 3: Month-One Strategic Review (60-90 minutes, after one month)
This is the retro where you assess whether the feature is viable long-term.
Big questions:
- Is this feature earning its cost? (Not in abstract value, but in measurable business impact.)
- Is the quality good enough, or are we accumulating technical and trust debt?
- Can we sustain this at 5x or 10x current usage?
- What did we learn about building AI features that applies to our next one?
This pass should produce strategic decisions: invest more, optimize and maintain, or rethink the approach. It should also produce a list of lessons learned that's specific enough to actually be useful next time.
Cost Surprises and What to Do About Them
Cost overruns are the single most common problem in AI feature launches. Here are the patterns and practical responses:
The chatty user problem. A small percentage of users generate a disproportionate amount of token usage. If 5% of users account for 40% of costs, you need to decide whether to rate-limit heavy users, optimize for their use case, or accept the cost.
The bloated context problem. You're sending more context to the model than you need. Review your prompts and system messages — are there instructions the model doesn't need for most requests? Can you dynamically include context only when relevant?
The "we forgot about retries" problem. Failures trigger retries, retries cost tokens, and under load, retry storms can multiply your costs. Implement exponential backoff and consider whether a failed request should retry at all or just return a graceful error.
The model overkill problem. You're using your most capable (and expensive) model for tasks that a smaller, cheaper model handles perfectly well. Route simple requests to cheaper models. Classify the task first, then choose the model.
Lessons That Transfer to Every AI Launch
After going through several AI feature launches, some patterns consistently emerge:
Your test suite was too clean. Real-world inputs are messier, longer, weirder, and more adversarial than anything you tested. Build a collection of "weird real inputs" after each launch and add them to your test suite.
Users will tell you what the feature should actually do. The way people use your AI feature often diverges from your design intent. Pay attention to that divergence — it's free product research.
Speed matters more than you think. Users have lower latency tolerance for AI features than you'd expect. If it takes more than a few seconds, they start to disengage. Perceived performance improvements (streaming responses, progress indicators) help a lot.
You overestimated V1 and underestimated V3. The first version of an AI feature is rarely impressive to users. But the third version, after two rounds of improvement driven by real usage data, often exceeds expectations. Ship V1 knowing it's a learning vehicle, not the final product.
Try NextRetro free — Structure your AI launch retro with phased columns and vote on which post-launch issues to tackle first.
Last Updated: February 2026
Reading Time: 8 minutes
