SOLID in simple words: how to build code that will not fall apart with the growth of the project

When a person first hears the word SOLID, it almost always sounds like something unpleasantly academic. It seems that now will begin a conversation for architects with twenty years of experience, where they will argue about patterns, abstractions and the right form of the interface. A beginner at this point usually thinks very simply: I would generally make a site, bot or API so that at least it works.

But in real development, the problem is almost never that the code failed to run. You can launch a lot. The real problem starts in a few days or weeks as the project grows. Suddenly it turns out that a small edit breaks the neighboring scenario, the new functionality has to be built in through force, the same file is responsible for everything, and the AI writes code differently for each new task, because the project itself no longer gives clear boundaries.

This is where SOLID turns out to be not a theory for the sake of theory, but a very practical thing.

If you explain it quite simply, SOLID is needed so that the code was not afraid to change. Not perfect to decorate. Don't turn it into an architecture museum. Namely, change: add new logic, replace external services, connect another AI provider, take heavy tasks in line without breaking half the system on the way.

For an AI development site, this topic is especially important. AI can very quickly generate working-looking code. That's his strength. But there's also the risk. When code is written too fast, bad structure builds up too quickly. If before a person at least tired of saying extra, then the model does not get tired. It easily creates a new service, a new wrapper, an extra level of abstraction, a strange universal helper and a huge interface for the future. Therefore, in AI development, architectural principles become not a luxury, but a way to hold the project in hand.

In this article, we calmly and consistently analyze what SOLID is, why a beginner needs it, how to understand each principle without memorizing wording, and how to apply these ideas in projects where part of the code is written by AI.

## What is SOLID in Human Language

SOLID is the five principles of code design that are often used as a reference when a project begins to become larger than a pair of functions in a single file.

They are deciphered as follows:

• S - Single Responsibility Principle
• O - Open/Closed Principle
• L - Liskov Substitution Principle
• I - Interface Segregation Principle
• D - Dependency Inversion Principle

If you read these titles as a beginner, they explain little. Therefore, it is better to translate everything into normal language.

Solid says something like this.

It is better to divide the code by roles, rather than put everything in one pile. It is better to make the system so that it can be expanded without rewriting the stable kernel. If you have a common contract, its implementation should not be unpredictable. You don’t have to make a module depend on a huge interface if it only needs a small part. Finally, business logic should not be tied to a specific database, SDK, or external service.

It sounds voluminous, but essentially all five principles are trying to address the same pain: making changes to the project local and understandable.

In a live project, requirements almost always change. You have one data source today, another one tomorrow. Today one AI-model, tomorrow you need to add a fallback, the day after tomorrow make the generation in line, then connect logging, cache, moderation, admin. If the code is designed from the beginning so that each new task spreads over ten files without clear logic, the project begins to slow down before it really grows.

SOLID does not make code "eternal" and does not guarantee a perfect architecture. It just makes good rules that reduce the likelihood of chaos.

## Why SOLID has become particularly important in AI development

When a project is written by a person, they usually feel the cost of complexity. He understands that a new layer of abstraction will have to be maintained, a new service will have to be read, a new interface will have to be explained. Therefore, even an inexperienced developer sometimes intuitively avoids unnecessary.

The AI doesn't have that intuition. He can come up with a solution that looks solid on the outside, but is already overloaded on the inside:

• business logic is mixed with API calls;
• a specific SDK is stitched directly into the use case;
• one class is responsible for generating, storing, notifying and logging;
• under a common interface, things that are generally different are combined;
• for the sake of a small task, five new entities are added that are not needed anywhere else in the project.

The worst part is that code often works. It starts, passes the happy path and even gives the impression of an “adult decision”. But when you start to develop the project further, it turns out that it is difficult to rely on it.

Therefore, SOLID in AI development is useful in two roles at once.

First, as a way to design code before generation. That is, you set a frame in advance: do not mix roles, do not sew the use case to the SDK, do not make a greasy interface for all AI capabilities at once.

Second, as a way to check the code already generated. If a model sends a solution, you look not only at whether it works, but whether that structure will break the project in a week.

That is why the topic of SOLID for an AI-site is not a classic conversation “about OOP from a book”, but quite applied material on how not to drown in the rapidly growing code.

## Start with the main idea: SOLID is not about sacred rules

Before you analyze the principles one by one, it is important to remove one dangerous misconception. Many people familiarize themselves with SOLID as if it were a set of mandatory architectural precepts. Because of this, then there are people who add interfaces everywhere, break down the code into endless files, and believe that the more complex the structure, the more “right” the project.

This is the wrong way.

SOLID is not a religion. It's a set of healthy limiters. It doesn't help everywhere or the same way. For a one-time script of fifty lines, it is almost unnecessary. For an MVP, it is not necessary to build a temple of abstractions for the evening. But once a project begins to live, change behavior, and interact with the outside world, these principles become dramatically useful.

Therefore, it is better to read further with this attitude: we do not learn to “implement SOLID”, we learn to notice typical architectural problems and solve them in a more mature way.

## Single Responsibility Principle: One role is better than porridge

The first principle is usually translated as the principle of sole responsibility. It’s often said that “a class should do one thing,” but that’s too crude an oversimplification. A more useful thought is that the module should have one clear reason to change.

Imagine a typical situation in an AI project. You have a ArticleService file that seems convenient at the start. It accepts an article generation request, calls a model, cleans the text, builds an SEO title, saves the result to the database, sends a notification to Telegram and writes a log. At the start, it can even be joyful: everything is in one place, you do not need to look for anything.

The problem is that this file starts to change for too many reasons. The generation prompt has changed - you need to edit the file. The format of the article storage has changed - back there. Integration with Telegram has changed – there again. There are new SEO rules, too. Added retry for the AI API — again the same module.

That is, the same piece of code is responsible for several semantic roles at once. This almost always makes the system fragile.

When we talk about SRP, it’s not about breaking everything down into microscopic pieces. The challenge is to make the roles distinguishable. Text generation is one responsibility. Saving the article is different. Sending notifications is the third. Coordination of the overall publication scenario is the fourth.

In a normal structure, it can look like this: a single module generates text, a separate repository stores material, a separate component builds metadata, a separate notifier sends an event, and a use case manages the order of these actions. Then each part has a clear zone, and changing one does not have to break the others.

Beginners often make two opposite mistakes here. The first is to bring everything into one universal service. The second is to go to the other extreme and start creating a file for each method. Neither of them helps. A good separation is not the maximum fragmentation, but semantic clarity.

If after reading the name of the file or class, it is not clear what it is responsible for, this is a signal. If the module is difficult to test without raising the half of the project, this is also a signal. If one task involves editing a piece where both HTTP, and the database, and business rules, and logging sit, most likely SRP is violated there.

For AI development, this principle is also useful because AI is better at narrow tasks than amorphous ones. When you ask the model to “change the logic of summary generation without touching publish and save”, it is much easier to work in a project where these roles are already separated.

## Open/Closed Principle: Good code expands, not breaks

The second principle is that entities should be open to expansion, but closed to modification. The wording again seems vague, but in real life the idea is simple.

When a new variant of an already familiar behavior appears in the system, it is good to add it locally, rather than rewrite the proven code in several places at once.

Let’s take a familiar example for AI projects. At first, your service can only send notifications to Telegram. Then there's the email. Then push. If all the logic of notifications is smeared through the application through conditional constructions, you quickly find yourself in a situation where when you add a new channel you have to climb into the old code that already serves working scenarios.

A more mature approach is to single out a common notification contract and different implementations for specific channels. Then adding email does not mean rewriting the core of publishing an article or order. You just add a new implementation to an understandable expansion point.

Crucially, OCP doesn’t call for extensibility “just in case.” This is a common trap, especially when working with AI. The model likes to come up with architecture for the distant future: registry, plugin system, universal factory layer and other beautiful designs, although the project still needs one simple integration.

Normal application of OCP begins not when you can imagine a thousand possibilities, but when the axis of change is already visible. For example, you understand that the AI provider can change. There will be several payment methods. Or that the publication will go to different external channels. Then it makes sense not to stitch the old logic firmly to the only option.

For a beginner, a simple guide here is useful: if a new similar feature almost certainly appears again, you should think about the expansion point. If not, don’t build complexity in advance.

In AI development, this is especially important because the world of tools is changing rapidly. Today you work with one text API, tomorrow you want to add another, the day after tomorrow you need a fallback on the local model. If the project is designed so that this extension turns into rewriting several layers at once, the code begins to resist growth.

## Liskov Substitution Principle: Contract must not lie

The third principle usually sounds the scariest. But what he's really talking about is a very practical thing: if there's a common contract in the system, it has to be predictable.

Simply put, if you say that several classes or modules implement the same type of role, then you can substitute them in that place without surprises.

In AI projects, it is very easy to break this principle because different external tools are similar only from afar. For example, you have the concept of TextGenerator. You expect any implementation to get prompt and return the text. It's a normal contract. But if you start to push into the same contract an entity that only works with images, or a model that can only respond in stream, or an adapter that always silently returns an empty line in case of an error, you get a false uniformity.

From the outside, everything seems beautiful: there is a common interface, different implementations, the architecture looks neat. But once one implementation begins to behave fundamentally differently, the client code faces surprises.

This is exactly what the Lisk substitution principle is trying to prevent. If something is called a text generator, it must honestly be able to play that role. If one implementation requires a completely different life cycle, a different login format, or a different error behavior, it’s likely a different contract.

Beginners often violate this principle not out of malice, but out of a desire to “do universally.” For example, create a common AIProvider, which is stuffed and the generation of text, and embedding, and moderation, and image generation, and speech-to-text. Formally, all this is related to AI, but in fact they are different roles. If each implementation then supports only a fraction of the methods, throws out errors on half the calls, or requires special exceptions, the architecture becomes deceitful.

The healthy way here is not to try to combine everything under one umbrella. If you really have different types of AI capabilities in the project, it is better to give them different contracts: a text generator, a moderator, an e-mailing provider, an image generator. Then each entity remains honest about its role.

This principle is useful not only for interfaces in classic OOP. It also works well at the module, function, adapter and external integration level. Whenever you promise a system a certain behavior, it’s important not to break that promise with the hidden features of a particular implementation.

## Interface Segregation Principle: Don't make it depend on the superfluous

The fourth principle continues the same line of honesty, but on the other hand. He says the module should not depend on methods it does not need.

In practice, this means not making giant combine interfaces if different parts of the system need different little pieces of behavior.

Imagine that the project has a common service AiPlatform. It contains everything at once: text generation, image generation, moderation checking, embeddings, speech-to-text, file uploading, tariff information and anything else on top. Now the article publication module needs only one method: get the text by prompt.

But because the interface is too broad, it seems to depend on the entire platform. He is dragging much more into his mental model than he really needs. Such a contract is worse read, worse tested, and worse explains the boundaries of the system.

Well-designed code often relies on small and understandable contracts. If a module only needs text generation, give it a dependency on a text generator. If the module only needs moderation, don’t make it know about image API and speech recognition.

For a beginner, this may seem excessively accurate, but in fact the principle is very practical. The longer the contract, the easier it is to understand why it is needed. The simpler the contract, the easier it is to replace, lock and check it. The less superfluous the module knows, the less reason it has to accidentally cling to unnecessary behavior.

AI here, too, tends to be wrong. He often creates a “main app service” where he stores everything. It looks convenient for the first half hour, but very quickly turns into an amorphous object that knows about the whole world at once. ISP helps keep such a monster from growing.

Simply put, this principle asks us to be humble. Do not describe more than is necessary for a particular role.

## Dependency Inversion Principle: Business logic should not be held hostage to details

The fifth principle is often considered the most complex, although in applied development it is one of the most useful. High-level logic should not depend on low-level technical details. Both must depend on abstraction.

If you translate this into normal language, you get this idea: the script for which your product exists does not have to be pinned tightly to a specific library, ORM, SDK or external API.

Let's say you have a script for "translate the article and save the translation." This is the domain or applied logic of the project. But if a specific OpenAI client, a specific JSON response structure, a specific mode of retracement, and the characteristics of a single external provider are directly sewn up inside this scenario, then an important business operation suddenly depends on the technical detail.

That might not bother you today. And tomorrow you want to replace the provider, add a fallback, test the logic without the network or move some of the work to the queue. The use case is inseparable from the external tool.

This is what the Dependency Inversion Principle protects against. A good use case does not know about the SDK, but about the role it needs. For example, he needs a translator, an article repository, an event logger. The specific implementation of these dependencies can change from the outside. The script itself should not become a collection of technically chaotic details.

It is important not to confuse the architectural principle with dependency injection as a technique. Addiction injection alone does not guarantee anything. You can carefully "transmit from the outside" and bad addiction. The point of DIP is not necessarily to create an interface for every object. The point is to separate politics from mechanics.

Politics is why the system does something. Mechanics - through which driver, SDK, ORM and protocol it does it.

For AI projects, this principle is especially valuable, because almost everything important there is connected with the outside world: models, queues, databases, CMS, Telegram, analytics, payments, storage. If domain logic is directly interlinked with each of these tools, the project quickly becomes heavy and unstable. If the dependencies are neatly removed beyond the boundaries of application scenarios, the code becomes survivable.

## How all five principles fit into one picture

Individually, SOLID may seem like a collection of disparate tips. In reality, the principles support each other well.

Single Responsibility helps to separate roles and not turn a module into a dump of responsibilities. Open/Closed makes you think about understandable growth points so that system expansion does not require constant hacking of stable parts. The Liskov Substitution ensures that our abstractions are not false. Interface Segregation prevents contracts from swelling to “everything in the world.” Dependency Inversion separates business logic from technical details.

When these ideas work together, the code becomes more relaxed. Not perfect, not sterile, but calm. It's easier to read. Change is easier to localize. Mistakes are easier to explain. AI is easier to integrate into such a project, because it has clear boundaries: where the role is, where the scenario is, where the infrastructure is, where the external service is.

This is especially important for sites and services that grow through short iterations. There is rarely the luxury of rewriting everything from scratch. It is much more valuable to have a project that can be gradually improved without breaking the foundation every time.

## How to apply SOLID to a beginner without inflection

The best strategy for a beginner is not to try to “do everything by SOLID” from the first file. So it is very easy to go into decorative architecture, where there are already a lot of abstractions, but there is still little benefit.

A different order is much more useful.

Make a minimum working version first. Then see where the code started to hurt. Is one file growing? Are there similar designs throughout the project? Is it difficult to replace a provider or test a scenario without a network? Are there interfaces that promise too much? Does business logic entail a specific SDK?

This is where the SOLID principles begin to make a real difference.

That is, not to “implement SOLID in the project”, but, for example, to separate data storage from the business scenario, break up too broad a contract for honest roles, remove direct dependence on a particular library from the use case, stop mixing AI response generation with notification delivery.

So SOLID stops being a bunch of letters and becomes a way to make more mature engineering decisions.

## How to use SOLID in AI in practice

If you’re writing code with AI, these principles are useful not only as a theory, but also as part of a prompt and review.

Before generation, you can clearly set a frame: do not mix business logic with integrations, do not twist the use case to a specific SDK, do not make one bold AI interface for unrelated tasks, do not add abstractions without a visible axis of expansion.

Once generated, you can check the code not only for “working or not,” but also for how much it respects the boundaries of roles. If a model sends a class that simultaneously validates data, calls an external API, writes to a database, converts a UI format, and sends an event, it is not “comfortably assembled,” but a signal to parse. If she made a common AIProvider interface, under which half of the implementations honestly can not support all methods, it is not universality, but false architectural purity. If the use case directly depends on the specific client of the external service, today it may not hurt, but tomorrow it will slow down the development.

The good news is that AI can write code closer to SOLID if given the right constraints. The problem is not that the model does not know architecture. The problem is usually that the project itself does not set clear rules, and the user does not require structural accuracy.

Therefore, in AI development, SOLID works particularly well as a problem-setting language. It helps not just to ask "make a feature", but to ask "make a feature so that it can be developed later.".

## Where not to fanatically drag SOLID

In order for the picture to be honest, the opposite must be said: there are situations where trying to use SOLID with full seriousness only hinders.

If you have a disposable local script that you need to run once and forget, do not build a layered architecture there. If you’re making a prototype for one evening, you don’t have to design a dozen interfaces right away. If the code is easier to remove than to accompany, sometimes a healthy straight-forward option is more useful than a beautiful architectural design.

But it’s important that it’s a deliberate oversimplification, not random chaos that then pretends to be an “MVP approach.” The SOLID principles do not require formality. They require honesty: to understand where the project is short-lived and where it is already beginning to accumulate inertia.

## Main conclusion

SOLID isn’t useful because it’s a famous acronym for architecture books. It's useful because it helps keep the code in a state where it can still be developed.

For a beginner, this is especially important. At the start, almost everyone thinks the main goal is to make the project work. But in real-world development, especially with AI, the real goal is a little different: to make sure that after the first working version of the project does not become a trap.

When code can grow without permanent breakdowns, when its parts are divided into roles, when contracts are honest, when modules do not know what is superfluous, and applied logic does not sit on the chain of a particular SDK, development becomes noticeably calmer. Not simpler in a magical sense, but clearer and more stable.

AI doesn't abolish architecture. On the contrary, it makes it even more important. Because a strong code generator can speed up both a good project and a bad one very quickly. SOLID is one of those tools that helps prevent this speed from becoming an accelerated accumulation of chaos.

If you remember one thought from the whole article, let it be this: good code is not the one that once worked, but one that can be changed without fear. This is why SOLID is worth studying.