Today, in the world of AI, there's a new phrase floating around: "vibe coding." It's the practice of using a large language model (LLM) to generate code with simple natural language prompts, and it can feel like a game-changer. The idea that you can bypass a high-cost engineering team and simply prompt your way to an AI product sounds like a dream for any business or product owner.
But as our Lead ML Engineer, Vitalijus, explained in a recent podcast, this "plug and play" mentality is an illusion. While a quick demo might look promising, the true cost of vibe coding for a production-grade AI solution can be far more expensive than investing in professional machine learning engineering expertise from the start.
So, before you greenlight a vibe coded project, let's explore why this seemingly easy shortcut is a recipe for disaster and what the real cost of hiring ML engineers looks like.
And if you want to check out the podcast episode, here it is!
Generative AI models are stochastic, meaning they are probabilistic and "guess" the next word or output based on patterns they've learned. This non-deterministic nature makes their outputs unpredictable and presents a major risk for businesses.
Vibe coding relies on systems that have been pre-trained on millions of lines of code of various quality levels. Because of this, the code doesn't adhere to good practices and can't be trusted in a production environment.
A vibe coded product may not include the necessary safeguards to prevent a user from asking the AI to delete data or access sensitive information. As seen in early chatbots, the lack of guardrails can lead to catastrophic results.
The AI can hallucinate, producing incorrect or nonsensical code because it lacks the deep understanding of business context and logic. In critical business applications, this unpredictable behavior can lead to significant losses or damage to your brand reputation.
LLMs are trained on historical data. If you ask a model to work with a new library or technology that wasn't in its training data, it can become unstable. One speaker noted that in some specific chats it starts hurting your brand, and you're not going to notice it because in general it's working.
One of the most critical engineering principles for a robust and safe AI system is the use of guardrails. These are not vague rules; they are manually created, deterministic protocols that act as a crucial, protective layer between the AI's output and your production systems.
These guardrails ensure the AI operates within predefined safety parameters, preventing it from producing undesirable or harmful results. Essentially, they serve as a moderation layer that "makes sure it's not going to break things," providing the predictability and control that "vibe coding" lacks. They are the physical manifestation of an engineering mindset.
To effectively implement guardrails, you need to think beyond simple filters and establish a multi-layered, strategic approach. This is where the engineering mindset is crucial, as you're not just adding a quick fix but building a robust system that can handle unpredictable inputs and outputs.
Here’s a breakdown of how you can implement guardrails for your AI product:
For a deeper dive, download our free guide Effective Guardrails for Your GenAI Apps
Many business owners might wonder why they can't simply train their existing software engineers in machine learning. While software engineers are highly capable, what machine learning engineers do is fundamentally different and requires years of dedicated experience.
A traditional software engineer's work is often clear-cut, where the code either works or it doesn't. But understanding what ML engineers do reveals a more complex reality: an ML model's success is less straightforward; it might work great for most users but fail in unexpected ways for others.
When developing an AI product, the difference between a software engineer and an ML engineer is the difference between writing the rules and teaching a system to create its own. A traditional software team can build the perfect, stable application around your AI, but they can't build the "brain" itself.
Here's how that plays out in practice:
The democratization of AI has made it excitingly easy to experiment and build initial prototypes. But for any organization serious about turning a promising AI project into a reliable, scalable product, the vibe coding mentality is a recipe for disaster. A quick demo can be misleading, and the true cost of a DIY approach often far outweighs the perceived savings, manifesting in delayed projects, unreliable systems, security vulnerabilities, and significant financial losses.
So, don't get stuck trying to make a vibe coded solution work. The smart move is to hire ML engineers with proven expertise. Investing in genuine machine learning engineering expertise, whether through a dedicated in-house team or a strategic partnership with a specialized vendor, is not an option but a necessity to avoid costly mistakes and ensure long-term success.
Ready to turn your AI vision into a secure, scalable product? Contact our team of professional ML engineers today to build a solution that works and lasts.
Today, in the world of AI, there's a new phrase floating around: "vibe coding." It's the practice of using a large language model (LLM) to generate code with simple natural language prompts, and it can feel like a game-changer. The idea that you can bypass a high-cost engineering team and simply prompt your way to an AI product sounds like a dream for any business or product owner.
But as our Lead ML Engineer, Vitalijus, explained in a recent podcast, this "plug and play" mentality is an illusion. While a quick demo might look promising, the true cost of vibe coding for a production-grade AI solution can be far more expensive than investing in professional machine learning engineering expertise from the start.
So, before you greenlight a vibe coded project, let's explore why this seemingly easy shortcut is a recipe for disaster and what the real cost of hiring ML engineers looks like.
And if you want to check out the podcast episode, here it is!
Generative AI models are stochastic, meaning they are probabilistic and "guess" the next word or output based on patterns they've learned. This non-deterministic nature makes their outputs unpredictable and presents a major risk for businesses.
Vibe coding relies on systems that have been pre-trained on millions of lines of code of various quality levels. Because of this, the code doesn't adhere to good practices and can't be trusted in a production environment.
A vibe coded product may not include the necessary safeguards to prevent a user from asking the AI to delete data or access sensitive information. As seen in early chatbots, the lack of guardrails can lead to catastrophic results.
The AI can hallucinate, producing incorrect or nonsensical code because it lacks the deep understanding of business context and logic. In critical business applications, this unpredictable behavior can lead to significant losses or damage to your brand reputation.
LLMs are trained on historical data. If you ask a model to work with a new library or technology that wasn't in its training data, it can become unstable. One speaker noted that in some specific chats it starts hurting your brand, and you're not going to notice it because in general it's working.
One of the most critical engineering principles for a robust and safe AI system is the use of guardrails. These are not vague rules; they are manually created, deterministic protocols that act as a crucial, protective layer between the AI's output and your production systems.
These guardrails ensure the AI operates within predefined safety parameters, preventing it from producing undesirable or harmful results. Essentially, they serve as a moderation layer that "makes sure it's not going to break things," providing the predictability and control that "vibe coding" lacks. They are the physical manifestation of an engineering mindset.
To effectively implement guardrails, you need to think beyond simple filters and establish a multi-layered, strategic approach. This is where the engineering mindset is crucial, as you're not just adding a quick fix but building a robust system that can handle unpredictable inputs and outputs.
Here’s a breakdown of how you can implement guardrails for your AI product:
For a deeper dive, download our free guide Effective Guardrails for Your GenAI Apps
Many business owners might wonder why they can't simply train their existing software engineers in machine learning. While software engineers are highly capable, what machine learning engineers do is fundamentally different and requires years of dedicated experience.
A traditional software engineer's work is often clear-cut, where the code either works or it doesn't. But understanding what ML engineers do reveals a more complex reality: an ML model's success is less straightforward; it might work great for most users but fail in unexpected ways for others.
When developing an AI product, the difference between a software engineer and an ML engineer is the difference between writing the rules and teaching a system to create its own. A traditional software team can build the perfect, stable application around your AI, but they can't build the "brain" itself.
Here's how that plays out in practice:
The democratization of AI has made it excitingly easy to experiment and build initial prototypes. But for any organization serious about turning a promising AI project into a reliable, scalable product, the vibe coding mentality is a recipe for disaster. A quick demo can be misleading, and the true cost of a DIY approach often far outweighs the perceived savings, manifesting in delayed projects, unreliable systems, security vulnerabilities, and significant financial losses.
So, don't get stuck trying to make a vibe coded solution work. The smart move is to hire ML engineers with proven expertise. Investing in genuine machine learning engineering expertise, whether through a dedicated in-house team or a strategic partnership with a specialized vendor, is not an option but a necessity to avoid costly mistakes and ensure long-term success.
Ready to turn your AI vision into a secure, scalable product? Contact our team of professional ML engineers today to build a solution that works and lasts.
