How to build an AI product
To build an AI product, define the user problem and the measure of success first. Decide whether AI is the right tool, choose the models and data, and build an evaluation set before the product. Add guardrails and a fallback, then launch to a small group and watch the results.
Published , updated 7 min read
What makes an AI product different from other software?
An AI product gives answers that can vary. The same input can produce a different output on another day, and the output can be wrong with no error message. Normal software tests check that a function returns the right value. AI products need a different check, because the right answer depends on the quality of the output.
This changes the build. The team needs a way to measure output quality before launch, a plan for wrong answers and a way to watch the product in use. The steps below follow that order.
Step 1: define the user problem and the success measure
Write one sentence that names the user, the task and the outcome. For example, a support agent needs a draft reply for a common question, within the time they already spend on it. A vague goal such as better customer service cannot be tested.
Then choose one measure of success. It can be the share of drafts that an agent sends without change, or the time saved on a task. Pick a measure you can collect from real use.
Step 2: decide whether AI is the right tool
Not every problem needs a language model. A rule, a search index or a form can solve some tasks more reliably and at lower cost. Ask whether the task needs flexible language handling, pattern recognition or summary. If the task has a fixed set of correct answers, a simpler tool may be better.
If AI is the right tool, state the limit in writing. Name the inputs the product accepts, the outputs it may give and the actions it must never take.
Step 3: choose models, data and architecture
Choose the model after you test candidates on your own examples. Public benchmark scores do not show how a model handles your documents, your tone or your edge cases. Compare answer quality, response time, data terms and cost for each candidate.
Decide how the model gets the information it needs. Some products send the whole task to the model. Others first search your documents and send only the relevant text. The choice affects cost, accuracy and how fast your data can change. The guide on RAG, fine-tuning and prompting covers this choice in detail.
Step 4: build an evaluation set before the product
An evaluation set is a list of real inputs, each with a rule for a good output. Collect it before you write the product logic. Include common cases, hard cases and cases where the right answer is to refuse or to hand off to a person.
Run the set after each change to the prompt, the data or the model. A change that fixes one case can break others. The set shows that before users see it.
Step 5: add guardrails and a fallback
Guardrails stop the product from giving unsafe or off-topic answers. They can limit the topics, check the output against rules and block some actions. A fallback is the response the product gives when the model fails, times out or cannot answer with confidence. A clear fallback, such as a handoff to a person, is better than an error page.
Log each request and response, with the reason for each block or fallback. The log is the main source of evidence when a user reports a problem.
Step 6: launch to a small group and watch the logs
Release the product to a small group of users first. Read the logs each day during this period. Look for answers that fail the evaluation rules, questions the product did not expect and users who leave the flow.
Open the product to more users only after the results match the success measure you set in step 1. Keep the evaluation set running after launch, so each later change is checked the same way.
What to ask a development partner before you start
A partner should be able to explain how it will measure output quality. Ask these questions before you sign a proposal.
- How will you build the evaluation set, and who supplies the example cases?
- Which models will you test, and how will you compare them on our data?
- What happens when the model fails, and what does the user see?
- What do we own at the end, including the code, prompts and evaluation set?
- How will you report output quality after launch?
Questions about this guide
Do I need a custom model to build an AI product?
Usually not. Most AI products use an existing model with clear instructions, your data and checks on the output. A custom model adds cost and maintenance. It is worth considering only when tests show that existing models cannot meet the quality you need.
How do I know the AI output is good enough to launch?
Set the quality bar before the build, using the evaluation set and the success measure. The product is ready for a small launch when it meets the bar on the evaluation set and the fallback works. Full launch follows when the logs show the same result with real users.
What is a fallback in an AI product?
A fallback is what the product does when the model cannot give a reliable answer. It can offer a handoff to a person, a link to a document or a clear message that the question needs review. The fallback protects the user and keeps the product usable.