The development of an MVP has always been a challenge. It was once as simple as creating a minimal viable product with only the most basic functionality and launching it as quickly as possible.
However, the introduction of AI to the process has made it more complex. As a founder, you may be able to build a language model over a single weekend that appears to work well in demos.
When real users begin interacting with the application by typing odd phrases or terms into it, the application will struggle to function properly. This struggle represents the difference between a functional demonstration of a product and one that is used repeatedly by users. The hiring decisions made at this point will greatly impact whether the product ultimately succeeds or fails.
Therefore, what are the best ways for a startup to hire MVP developers for an AI product? There are many variables involved in answering this question. These variables include the specific concept of the product, the financial resources available to the startup, the time frame in which the product needs to be developed and released, and the technical skills of the founders.
Start by knowing what you want to prove using your first version of MVP. It's important for all parties involved in hiring MVP developers to be clear about what you expect from the first version. Your first version of MVP is not simply a reduced form of the complete product; it's an experiment. To help with the process, here are some questions to consider:
Many startup companies discover during this phase of development that only one element in their first version of MVP requires the use of AI. As such, the other elements may remain relatively basic, which provides significant savings in terms of cost and time.
Many developers are able to develop based on a specification.
However, few are able to say that the specification is too large. When you're hiring an AI MVP Developer, look for someone who has the ability to question why a specific feature is required and has the creativity to suggest ways to cut features from the initial version of your product that don't provide value to early adopters.
Scope is what kills timelines in startups. Developers who have an understanding of the trade-offs of developing products typically save more time than those who simply type fast.
AI knowledge is important, but basic software development is also important. An AI feature will be integrated into an existing software application. The software application will continue to require a login process, a database, payment processing functionality, hosting options for the application, error handling capabilities and a well-designed user interface.
Candidates and teams should have experience with: Web and Mobile App Development Backend Development & APIs Databases & Cloud Hosting Authentication & User Management Testing & Deployment Security Practices A prototype may appear visually appealing.
However, if a prototype does not function properly under simultaneous use by multiple users, then an experienced startup MVP development team will demonstrate value.
While writing effective prompts are important skills to have, they do not necessarily define what skills are needed when evaluating a candidate’s qualifications for a specific role within your company.
Depending on the type of product that is being developed, some other types of relevant experiences may be: Working with language model APIs Retrieval from your company’s internal documents or data Embeddings and search Tool & function calling Structured output Selecting among models based upon cost and/or speed Evaluating the basic quality of outputs The right combination of these skills will vary by product. For example, a chat assistant, a document analysis tool and a recommendation system each require slightly different sets of skills.
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The best part of every interview is finding out how an AI product developer handles problems. AI products do give incorrect answers. The question is how the developer deals with this. Some example questions to ask are:
How will you determine why a result appeared "off?" Most people who have developed AI features previously will tell you actual examples of situations like these. Most people who have not will use general terms.
The costs of an AI MVP are different from those of an application without AI. Every time a model is called (requested), it may incur a cost; and a feature that seems inexpensive when tested by 10 users will become very expensive if called by 1,000.
Developers who consider these issues early on use smaller models to perform simple tasks, store previously computed responses (cache), and establish reasonable limits. Speed is also important. Start-up founders generally have a short period of time before they run out of funding.
Therefore, being able to deliver a product quickly (i.e. in weeks rather than months) will enable the team to obtain more lessons learned from their experiences.
An interview cannot show you how someone will handle a new problem. A good way to do this is by giving each candidate a small but realistic scenario (such as building a simple app) and having them explain: -
The purpose of this exercise isn't to get the "right" answer. The purpose of this exercise is to see how well the candidate is able to prioritize between speed, quality, cost, and risk.
That decision isn't always easy. For narrow projects, freelancers are generally quick and cost-effective; however, they may not provide full coverage if you're looking for someone who has the ability to handle design, back-end development, artificial intelligence (AI), and DevOps simultaneously.
Recruiting experienced employees to build your own in-house team requires time that many start-ups don't have. MVP development companies offer a compromise. They'll assemble a multi-disciplinary team with experience in each area you're seeking to develop. That makes managing a project much simpler than trying to manage individual specialists.
The downside of working with an MVP development company is that you'll want to establish expectations regarding ownership, handoffs, and access to code from the very beginning. Many startups will use a third-party contractor to complete their initial release of an MVP, and then hire additional staff internally once their project's direction becomes clearer.
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The last thing you want to do is ignore the handoff and ownership aspects. You should have checked these things before getting excited. Who owns the code, where will it be hosted, what kind of documentation will I receive and how easy would it be for another developer to pick up this project at some point in the future. Even though a product may work right now, if there is only one person that knows how it works then that product becomes very high-risk.
An AI-powered MVP is not solely developed based upon an AI model. Rather, it is developed by individuals who have knowledge regarding how the AI model interacts with data, users, cost, and the somewhat chaotic environment of early adopters.
Therefore, when hiring MVP developers, instead of focusing on the jargon associated with MVP development, you should identify those developers with strong product sense, solid engineering skills, an honest assessment of potential failures, and a plan for post-launch activities.
