How to Develop with AI: Basic Workflow

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When people first hear about AI development, they often get the wrong idea. It seems that it is enough to write one prompt - and the finished application will appear by itself.

In practice, things work differently.

AI development is an iterative process, very similar to conventional development. The only difference is that much of the code is generated by the model, and the developer manages the process through the task description.

The work takes place through a cycle:

idea → prompt → code generation → launch → fixes → deploy

This cycle can be repeated dozens of times until the project becomes operational.

In this article, we will examine:

• what Full Workflow Development Looks Like With AI
• what happens at every stage
• what roles do LLM, developer and runtime play
• why iterations are a normal part of the process
• how to avoid common mistakes

## Why AI development is a cycle, not a single request

The main mistake of beginners is the expectation that AI will immediately create the perfect solution.

But software development has always been a process of iterations:

1. come up
2. the first version is created
3. bug out
4. system improves

AI just speeds up the process.

If the developer used to write code manually, now he describes the problem, and the model generates code, which is then refined.

Therefore, workflow AI development is better represented as a cycle.

After deployment, the cycle can begin again when new features appear.

## Step 1. Idea and task setting

Every development starts with an idea.

It could be:

• Telegram-bot
• web-service
• automation
• API
• interior

It’s important to understand that AI works better with specific tasks than abstract queries.

### Bad task setting

AI doesn’t know what kind of website is needed.

### Good problem setting

The more clearly formulated the task, the better the result.

## Step 2. Prompt formation

The next step is to turn an idea into a prompt.

A prompt is a task description for AI.

It shall contain:

• context
• requirement
• technology stack
• expected outcome.

### An example of a good prompt

Create a Telegram bot on Node.js.

Functions:
- Team /start
- button menu
- ordering

Use PostgreSQL to store users.

Such a prompt gives the model enough information to generate the structure of the project.

## Step 3. Code generation

After receiving the prompt, the model generates the code.

It could be:

• project
• function
• aPI handlers
• configuration
• SQL tables.

For example, a Telegram bot model can create:

At this point, the code is usually not perfect. This is the first version.

The main goal is to quickly get a working basis.

## Step 4. Launch and verification

The next step is to start the code.

The developer checks:

• whether the project is compiled
• start-up
• whether functions work.

Errors often appear at this stage:

It's a normal part of the process.

AI development assumes that the code ** will be refined through iterations**.

## Step 5. Corrections and improvements

After the launch, the finalization phase begins.

Developer:

• corrective
• specifies
• ask AI to improve the code
• adds new features.

For example, you can ask AI:

Add error logging and exception handling.

Or:

Divide the code into modules and structure the project.

Each new iteration improves the system.

## Step 6. Deploy

When the application starts to work stably, it can be deployed.

Deployment is the publication of an application on a server or platform.

Examples of platforms:

After deploitation, the application becomes available to users.

## Roles in AI Development

To understand workflow more deeply, it is important to understand the roles of the participants in the process.

### LLM (model)

LLM is a code generation system.

It performs tasks:

• code-generate
• explain
• architecture
• finds mistakes.

But the model does not see the whole system and does not control the project.

It only works with the context it is given.

### Developer

The developer remains a central figure.

Its tasks:

• task
• code
• architecture
• make decisions.

In AI development, the developer becomes the architect and code editor, not just the author.

### Environment of implementation

The runtime environment is where the code works.

It could be:

• local
• server
• a cloud platform.

She's responsible for:

Without a runtime environment, code is just text.

## Complete AI development scheme

If you put it all together, you get the next model.

Work cycle:

This cycle is repeated until the product is stable.

## Why this approach works

AI development speeds up software creation for one simple reason.

It removes much of the **routine coding.

Developers spend more time on:

• architecture
• ideas
• refinement

Not writing boilerplate code.

This is particularly useful for:

## How to apply workflow through AI

To work effectively with AI development, it is important to correctly formulate tasks.

An example of a universal prompt:

I'm building a Telegram bot.

Need:
store users
implement teams
- add logging

Use Node.js and PostgreSQL.

Show the project structure and example tables.

This format helps the model generate more accurate solutions.

## Outcome

Developing with AI is not one request or the magic of code generation.

It is an iterative workflow in which the person and the model work together.

The main cycle looks like this:

Understanding this process is key to working effectively with AI tools.