What Breaks First When You Ship an LLM Feature to Real Users?
Five failure points we check before an AI feature meets users, and not one of them is the model.
A demo is one user, one happy path, and a prompt you wrote yourself. Real users are none of those things.
Before an AI feature meets users, we work through a five-point checklist we developed while building Goblyn, our autonomous AI platform that plans, writes, publishes, and optimizes a full brand blog. MagicTrips, our AI travel planner, is live in production too. The five points:
- User input that flows straight into a prompt
- Token spend with no ceiling
- Latency
- Output shapes the code did not expect
- No fallback when a model call fails
Three of the five are named in advance by the OWASP Top 10 for LLM Applications, first released in 2023 and updated for 2025, which gives teams shipping chatbots, copilots, and agents a ready-made list of failure modes to test against before launch.
User input flows straight into the prompt
In a demo, you write the prompt. In production, users write part of it. Anything a person types, pastes, or uploads can end up inside instructions the model treats as authoritative. OWASP ranks prompt injection as the number one risk in its 2025 list for LLM applications, and states outright that it is unclear if there are fool-proof methods of prevention.
That makes it a containment question rather than a fix: what is the model allowed to do with the text it reads, and what happens when someone tries to make it do something else. It is the first point on the checklist, and it gets checked before the feature meets users, while the design is still cheap to change.
Token spend has no ceiling
Model calls bill per token, and OWASP gives uncapped inference its own category: LLM10 Unbounded Consumption, which includes what it calls Denial of Wallet, where attackers exploit the cost-per-use model of cloud-based AI services. The feature works. The bill is the bug.
Goblyn plans, writes, publishes, and optimizes a full brand blog, which means chained model calls moving through a publishing pipeline without a person sitting between the steps. So the checklist asks the boring questions early. What is a single job allowed to cost? What stops a retry loop? What does one tenant get before something says no?
Latency is a product problem, not a benchmark
Response time looks fine when you are the only person testing. It looks different inside a flow a user is waiting on, especially when the request touches more than one slow service. MagicTrips is an AI travel planner built with a Google Places integration alongside its model work, so a single user action can fan out.
The checklist treats latency as a design decision. What does the user see while they wait? What work can move to the background? Which part of the answer can arrive first? Answer those before launch, not after the first complaint.
The output shape is not what your code expected
Most LLM features are not chat. They feed something downstream: a database write, a UI component, another model call, a publishing step. That makes the output a contract, and one missing field or one extra paragraph of preamble surfaces three steps later where it is hard to trace.
It is also a security problem, not only a parsing bug. OWASP classifies handing unvalidated model output downstream as Improper Output Handling, LLM05, and lists consequences including XSS and CSRF in web browsers, SSRF, privilege escalation, and remote code execution. Validate at the boundary, reject what does not fit, and decide in advance what happens to the rejects.
There is no fallback when a model call fails
If the feature has one path and that path is a model call, then the feature is down whenever the call is. The checklist asks what the degraded behavior is: a cached result, a simpler path that does not use a model, a queued retry, an honest message. Any of them beats a spinner. MagicTrips is live in production and generating affiliate revenue, so "the model was unavailable" is not something a user should have to absorb.
Why this belongs before the build, not after the launch
None of this is exotic, and that is the point. The failure modes are published in advance as a framework you can test against before launch. Testing them after launch means finding out in front of users.
On an AI project, our first fixed milestone is usually a feasibility check. Scope and feasibility get worked through with an LLM before a build is committed, so the decision to build rests on evidence rather than enthusiasm. When the evaluation points to a rules engine and a good search index rather than a model, we build that instead. Milestones stay small, each one has a named deliverable and its own demo, and a closed milestone means shipped, reviewed, and finished, never "mostly working, we will circle back."
AI-native builds are our specialty, and we back that with our own products running in production rather than adjectives. You work directly with Alec from first idea to launch: strategy, design, and full-stack engineering from one partner under one roof, no account managers, no hand-offs. Work is scoped into fixed milestones with pricing agreed before the build starts, so you know what ships and when. Send us the rough shape of your feature and a rough budget range. We read every inbound message and reply within two business days. Great software starts at zero. We get you to one.
- The five points we check before an AI feature meets users: user input that flows straight into a prompt, token spend with no ceiling, latency, output shapes the code did not expect, and no fallback when a model call fails.
- OWASP ranks prompt injection as the number one LLM risk and says it is unclear whether fool-proof prevention exists, so plan for containment instead of a fix.
- Handing unvalidated model output downstream is OWASP's LLM05 Improper Output Handling, with consequences including XSS, CSRF, SSRF, privilege escalation, and remote code execution.
- Uncapped inference has a name: OWASP's LLM10 Unbounded Consumption, including Denial of Wallet, where attackers exploit the cost-per-use model of cloud AI services.
- On an AI project, our first fixed milestone is usually a feasibility check, and when the evaluation points to a rules engine and a good search index rather than a model, we build that instead.
- OWASP ranks prompt injection as the number one risk in its 2025 LLM Top 10 and states it is unclear if there are fool-proof methods of prevention, making containment the goal.
- OWASP classifies passing unvalidated LLM output downstream as Improper Output Handling (LLM05), with consequences including XSS, CSRF, SSRF, privilege escalation, and remote code execution.
- OWASP's LLM10 Unbounded Consumption covers uncapped token spend, including Denial of Wallet, where attackers exploit the cost-per-use model of cloud-based AI services.
- The OWASP GenAI Security Project publishes a dedicated Top 10 for LLM applications, first released in 2023 and updated for 2025, giving teams building chatbots, copilots, and agents a ready-made checklist of failure modes to test against before launch.
Where does the five-point checklist come from?
We developed it while building Goblyn, our autonomous AI platform that plans, writes, publishes, and optimizes a full brand blog, including multi-tenant architecture, custom domains, subscriptions, and the publishing pipeline. MagicTrips, our AI travel planner, is live in production as well. The five points are user input that flows straight into a prompt, token spend with no ceiling, latency, output shapes the code did not expect, and no fallback when a model call fails.
What if the feasibility check says we do not need an LLM?
Then we say so. Scope and feasibility are worked through with an LLM before a build is committed, so the decision rests on evidence rather than enthusiasm. When the evaluation points to a rules engine and a good search index rather than a model, we build that instead.
Can to1 Labs work on an LLM feature we already shipped?
Yes. One of our engagement types is leveling up an existing product with new features, performance work, AI capabilities, or rescue work on a stalled codebase. The work is mapped into fixed milestones with clear pricing before the build starts, and the contact form asks for a rough budget range rather than listing rates. We read every inbound message and reply within two business days.