Large Language Models, commonly called LLMs, can generate text, answer questions, summarize documents, write code, analyze information, and power AI chatbots.
You can use LLMs directly through applications such as ChatGPT, but developers can also connect LLMs to their own applications using an API.
With Python and an LLM API, you can build applications such as:
- AI chatbots
- Writing assistants
- Code assistants
- Document summarizers
- Question-answering systems
- AI-powered search tools
- Customer-support systems
- Content generators
- Data extraction tools
In this guide, you will learn what an LLM API is, how it works, how to connect to one using Python, how to send prompts, receive responses, protect your API key, handle errors, and build a simple chatbot.
What Is an LLM?
An LLM, or Large Language Model, is an artificial intelligence model trained to understand and generate human language.
LLMs can perform tasks such as:
- Answering questions
- Generating text
- Summarizing content
- Translating languages
- Writing and explaining code
- Extracting information
- Classifying text
- Creating conversational responses
Examples of companies offering LLM APIs include OpenAI, Anthropic, Google, and several other AI providers.
Instead of running these large models directly on your computer, your application can send requests to an online API.
What Is an LLM API?
An LLM API is an interface that allows your application to communicate with a Large Language Model.
Your Python application sends a request containing a prompt.
The AI provider processes the prompt using an LLM and returns the generated response.
The basic process looks like this:
Python Application
↓
API Request
↓
Large Language Model
↓
AI Response
↓
Python Application
For example, your Python application might send:
Explain machine learning in simple words.
The LLM could return:
Machine learning is a type of artificial intelligence that allows computers to learn patterns from data and make predictions or decisions.
Your Python program can then display, store, or further process that response.
Why Use an LLM API with Python?
Python is one of the most widely used languages for artificial intelligence and machine learning applications.
It is particularly useful for LLM applications because Python has a simple syntax and a large ecosystem of AI-related libraries.
Using Python with an LLM API allows developers to quickly build AI-powered applications without training a large language model themselves.
For example, you could build a program that accepts a question:
What is Flutter?
Then sends that question to an LLM and displays the generated answer.
What You Need Before Using an LLM API
Before starting, you usually need:
- Python installed on your computer
- An account with an LLM provider
- An API key
- The provider’s Python package or HTTP API
- A code editor such as VS Code
You should also understand basic Python concepts such as:
variables
functions
imports
strings
Advanced machine learning knowledge is not required.
Step 1: Check Your Python Installation
Open your terminal and run:
python --version
Depending on your operating system, you may need:
python3 --version
You should see a Python version displayed.
For example:
Python 3.12.5
Current official OpenAI and Anthropic Python SDKs require modern Python versions, so using a recent Python 3 release is recommended.
Step 2: Create a Python Project
Create a new folder for your project.
For example:
mkdir llm_python_app
Move into the folder:
cd llm_python_app
Create a Python file:
touch main.py
Your project might initially look like this:
llm_python_app/
│
└── main.py
Step 3: Create a Virtual Environment
Using a virtual environment is a good practice because it keeps project dependencies separate.
Create one using:
python -m venv venv
On macOS or Linux, activate it with:
source venv/bin/activate
On Windows:
venv\Scripts\activate
Once activated, packages installed with pip will remain isolated inside the project environment.
Step 4: Install an LLM Python SDK
Many AI providers offer official Python SDKs.
For this tutorial, we will use the OpenAI Python library as an example.
Install it using:
pip install openai
The official OpenAI Python SDK currently uses the Responses API as the primary interface for generating model responses.
You can confirm that the package is installed with:
pip show openai
Step 5: Get an API Key
An API key allows your Python application to authenticate with the AI provider.
It usually looks like a long secret string.
You should treat an API key like a password.
Never publish your API key in:
- GitHub repositories
- Public websites
- Screenshots
- Tutorials
- Frontend JavaScript
- Shared source code
A bad approach would be:
api_key = "your-real-secret-api-key"
If this code is uploaded publicly, someone may be able to use your account’s API access.
Instead, store API keys in environment variables.
Step 6: Store the API Key Securely
A common method is to use a .env file.
Install the Python dotenv package:
pip install python-dotenv
Create a file named:
.env
Add your API key:
OPENAI_API_KEY=your_api_key_here
Then create a .gitignore file and add:
.env
This prevents Git from intentionally tracking your .env file.
Your project could now look like:
llm_python_app/
│
├── .env
├── .gitignore
├── main.py
└── venv/
Step 7: Load the API Key in Python
Inside main.py, import the required packages:
import os
from dotenv import load_dotenv
from openai import OpenAI
Load the environment variables:
load_dotenv()
Then create the API client:
client = OpenAI(
api_key=os.getenv("OPENAI_API_KEY")
)
The official SDK also supports reading OPENAI_API_KEY from an environment variable directly.
Step 8: Send Your First Prompt
Now you can send a request to an LLM.
Example:
import os
from dotenv import load_dotenv
from openai import OpenAI
load_dotenv()
client = OpenAI(
api_key=os.getenv("OPENAI_API_KEY")
)
response = client.responses.create(
model="gpt-5.5",
input="Explain artificial intelligence in simple words."
)
print(response.output_text)
The API receives your prompt, sends it to the selected model, and returns a response.
The current OpenAI Python SDK exposes generated text through response.output_text.
Understanding the Code
Let’s break the example into smaller pieces.
Import OpenAI
from openai import OpenAI
This imports the API client.
Create the Client
client = OpenAI()
The client is responsible for communicating with the API.
If your API key is already stored in the expected environment variable, the SDK can read it automatically.
Send the Request
response = client.responses.create(
model="gpt-5.5",
input="Explain artificial intelligence in simple words."
)
Here:
model
selects the model that should process the request.
And:
input
contains the user’s prompt.
Display the Response
print(response.output_text)
This extracts the generated text and prints it in the terminal.
Run the Python Program
Run:
python main.py
Or:
python3 main.py
You may receive a response similar to:
Artificial intelligence is technology that allows computers to perform tasks that normally require human intelligence, such as understanding language, recognizing patterns, and making decisions.
You have now connected a Python application to an LLM.
Accept User Input
Instead of using a fixed prompt, you can let users type their own questions.
Example:
import os
from dotenv import load_dotenv
from openai import OpenAI
load_dotenv()
client = OpenAI()
user_prompt = input("Ask AI something: ")
response = client.responses.create(
model="gpt-5.5",
input=user_prompt
)
print("\nAI:")
print(response.output_text)
Run the program:
python main.py
Then enter something like:
Ask AI something: What is Python?
The AI response will be displayed underneath.
Add Instructions to the LLM
You can provide higher-level instructions describing how the model should respond.
For example:
response = client.responses.create(
model="gpt-5.5",
instructions="You are a programming teacher. Explain everything in simple language for beginners.",
input="What is an API?"
)
print(response.output_text)
Here, the instruction tells the model what style and role to follow.
The input contains the actual user question.
Why Instructions Matter
Compare these two requests.
Simple:
input="Explain Docker."
More controlled:
instructions="""
You are a programming instructor.
Explain concepts for complete beginners.
Use simple language and practical examples.
""",
input="Explain Docker."
The second version gives the LLM more context about how the answer should be generated.
This is closely related to prompt engineering.
Create a Reusable Python Function
Instead of repeating the API request every time, create a function.
import os
from dotenv import load_dotenv
from openai import OpenAI
load_dotenv()
client = OpenAI()
def ask_ai(prompt):
response = client.responses.create(
model="gpt-5.5",
input=prompt
)
return response.output_text
answer = ask_ai("What is machine learning?")
print(answer)
Now you can reuse:
ask_ai()
for different prompts.
For example:
print(ask_ai("What is Flutter?"))
Or:
print(ask_ai("Explain REST APIs."))
Build a Simple Command-Line AI Chatbot
You can combine a loop with an API request to create a basic chatbot.
import os
from dotenv import load_dotenv
from openai import OpenAI
load_dotenv()
client = OpenAI()
print("AI Chatbot")
print("Type 'exit' to stop.\n")
while True:
user_message = input("You: ")
if user_message.lower() == "exit":
print("Chat ended.")
break
response = client.responses.create(
model="gpt-5.5",
instructions="You are a helpful AI assistant.",
input=user_message
)
print("AI:", response.output_text)
A conversation might look like:
You: What is Flutter?
AI: Flutter is an open-source UI framework used to build applications for multiple platforms from a single codebase.
You: Which language does Flutter use?
AI: Flutter primarily uses Dart.
You: exit
Chat ended.
Does This Chatbot Remember Previous Messages?
The simple chatbot above sends each user message separately.
That means previous conversation context may not automatically be included in every new request.
For applications requiring conversation memory, developers typically maintain conversation state or use provider-supported mechanisms for continuing responses.
A simplified approach is to store messages yourself.
For example:
conversation = []
Whenever a user sends a message:
conversation.append({
"role": "user",
"content": user_message
})
And after receiving an answer:
conversation.append({
"role": "assistant",
"content": ai_answer
})
You can then supply the relevant conversation history when generating later responses.
Be careful with extremely long conversations because larger context can increase token usage and cost.
What Are Tokens?
LLM APIs do not usually process text based purely on character or word count.
Instead, text is divided into smaller units called tokens.
For example, a sentence such as:
Python is great for AI development.
is broken into tokens internally.
API usage may count:
- Input tokens
- Output tokens
- Cached tokens
- Reasoning or other model-specific tokens
depending on the model and provider.
Tokens are important because they can affect:
- API cost
- Context limits
- Response length
- Application performance
What Is a Context Window?
The context window is the amount of information an LLM can consider during a request.
This can include:
- Instructions
- User prompts
- Previous conversation
- Retrieved documents
- Code
- Other supplied information
Suppose you send a long conversation containing thousands of messages.
Eventually, you may need to:
- Remove unnecessary history
- Summarize older messages
- Retrieve only relevant information
- Start a fresh conversation context
Proper context management is important when building production AI applications.
Set the Model Instructions
For consistent application behavior, you can define clear instructions.
For example:
SYSTEM_INSTRUCTIONS = """
You are an AI assistant for beginner Python developers.
Rules:
- Use simple English.
- Explain technical words.
- Include short examples when useful.
- Avoid unnecessary advanced concepts.
"""
Then use them:
response = client.responses.create(
model="gpt-5.5",
instructions=SYSTEM_INSTRUCTIONS,
input="What is a Python dictionary?"
)
This is much cleaner than rewriting the same instructions for every request.
Generate Content with an LLM API
LLM APIs can be used for content generation.
For example:
prompt = """
Write a beginner-friendly explanation of REST APIs.
Include:
- What a REST API is
- How requests work
- HTTP methods
- Status codes
- One practical example
Keep the explanation under 700 words.
"""
response = client.responses.create(
model="gpt-5.5",
input=prompt
)
print(response.output_text)
This could be used in:
- Content-management tools
- Blogging platforms
- Writing assistants
- Internal documentation systems
Summarize Text with an LLM API
You can also send existing text for summarization.
article = """
Artificial intelligence allows computers to perform tasks
that traditionally required human intelligence...
"""
prompt = f"""
Summarize the following text in three short sentences:
{article}
"""
response = client.responses.create(
model="gpt-5.5",
input=prompt
)
print(response.output_text)
This approach can be used to summarize:
- Articles
- Reports
- Meeting notes
- Emails
- Documentation
- User reviews
Use an LLM API for Code Explanation
LLMs can explain programming code.
Example:
code = """
def multiply(a, b):
return a * b
"""
prompt = f"""
Explain the following Python code to a beginner:
{code}
"""
response = client.responses.create(
model="gpt-5.5",
input=prompt
)
print(response.output_text)
The AI could explain what the function does, what parameters are, and how return works.
Extract Structured Information
One powerful use case for LLM APIs is extracting information from text.
Suppose you have:
Customer: John
Product: Wireless Mouse
Price: $29
Rating: 4.5
You can ask the model:
prompt = """
Extract the customer, product, price, and rating
from the following text.
Return the result as JSON.
Customer: John
Product: Wireless Mouse
Price: $29
Rating: 4.5
"""
The intended output might resemble:
{
"customer": "John",
"product": "Wireless Mouse",
"price": 29,
"rating": 4.5
}
For production applications, use your provider’s structured-output features where available instead of relying only on instructions such as “return JSON.”
What Is Structured Output?
Structured output means asking the model to return information in a predictable format.
For example:
{
"title": "Python Guide",
"category": "Programming",
"difficulty": "Beginner"
}
Structured outputs are especially useful when AI-generated information needs to be processed by software.
For example:
LLM
↓
JSON
↓
Python
↓
Database
Without structured data, your application may need to parse unpredictable natural-language responses.
Handle API Errors
API calls can fail.
Common reasons include:
- Incorrect API key
- Network failure
- Invalid request
- Rate limits
- Unsupported model
- Server errors
You should therefore use error handling.
Example:
try:
response = client.responses.create(
model="gpt-5.5",
input="Explain Python."
)
print(response.output_text)
except Exception as error:
print("Something went wrong:")
print(error)
For larger applications, it is better to handle specific exceptions separately.
Current OpenAI SDK error categories include authentication errors, rate-limit errors, bad requests, connection errors, and server/API-status errors.
Example of Better Error Handling
from openai import (
OpenAI,
AuthenticationError,
RateLimitError,
APIConnectionError,
)
client = OpenAI()
try:
response = client.responses.create(
model="gpt-5.5",
input="What is machine learning?"
)
print(response.output_text)
except AuthenticationError:
print("Invalid API credentials.")
except RateLimitError:
print("The API rate limit has been reached.")
except APIConnectionError:
print("Unable to connect to the API.")
except Exception as error:
print("Unexpected error:", error)
Production applications should also log errors appropriately.
Use Async API Calls
Normal Python code usually waits for an API response before continuing.
For applications making many requests, asynchronous programming can improve concurrency.
The official OpenAI Python library currently provides an AsyncOpenAI client.
Example:
import asyncio
from openai import AsyncOpenAI
client = AsyncOpenAI()
async def ask_ai():
response = await client.responses.create(
model="gpt-5.5",
input="Explain Python async programming."
)
print(response.output_text)
asyncio.run(ask_ai())
Async APIs are especially useful in applications such as:
- FastAPI backends
- AI chat systems
- Multi-user applications
- Applications processing many requests
Beginners do not need async programming for their first LLM project.
Streaming LLM Responses
You may have noticed that AI chat applications often display answers gradually instead of waiting for the entire response.
This technique is called streaming.
Instead of:
Request
↓
Wait
↓
Complete response
Streaming works more like:
Request
↓
First response chunk
↓
Next chunk
↓
Next chunk
↓
Finished
The OpenAI Python SDK supports streaming response events.
Streaming is particularly useful for chat interfaces because users can begin reading while the answer is still being generated.
LLM API with Flask
You can connect an LLM API to a Python backend framework.
For example, with Flask:
pip install flask openai python-dotenv
A simplified API endpoint could look like:
from flask import Flask, request, jsonify
from openai import OpenAI
app = Flask(__name__)
client = OpenAI()
@app.route("/ask", methods=["POST"])
def ask_ai():
data = request.get_json()
prompt = data.get("prompt")
response = client.responses.create(
model="gpt-5.5",
input=prompt
)
return jsonify({
"answer": response.output_text
})
if __name__ == "__main__":
app.run(debug=True)
Your frontend application could then send:
{
"prompt": "What is artificial intelligence?"
}
The Flask server sends the question to the LLM and returns the generated response.
LLM API with FastAPI
FastAPI is another popular framework for Python APIs.
Install:
pip install fastapi uvicorn openai
Example:
from fastapi import FastAPI
from pydantic import BaseModel
from openai import OpenAI
app = FastAPI()
client = OpenAI()
class PromptRequest(BaseModel):
prompt: str
@app.post("/ask")
def ask_ai(data: PromptRequest):
response = client.responses.create(
model="gpt-5.5",
input=data.prompt
)
return {
"answer": response.output_text
}
Run the server:
uvicorn main:app --reload
Now your frontend, mobile application, or another service can communicate with your Python AI backend.
Recommended Architecture for an LLM Application
Avoid calling a secret-key LLM API directly from a public frontend or mobile application.
A better architecture is:
Flutter / React / Website
↓
Your Backend
Python / Node.js
↓
LLM API
↓
AI Response
↓
Your Backend
↓
Frontend
The API key remains on your server.
This helps prevent users from extracting your secret API credentials from a frontend application.
Example Project Structure
As your application grows, avoid keeping everything inside one Python file.
A simple structure could be:
ai_app/
│
├── app/
│ ├── main.py
│ ├── services/
│ │ └── llm_service.py
│ └── prompts/
│ └── prompts.py
│
├── .env
├── .gitignore
└── requirements.txt
llm_service.py could contain:
from openai import OpenAI
client = OpenAI()
def generate_response(prompt: str) -> str:
response = client.responses.create(
model="gpt-5.5",
input=prompt
)
return response.output_text
Then other parts of your application can call:
generate_response("Explain REST APIs.")
This structure is easier to maintain.
Store Dependencies in requirements.txt
You can save project dependencies using:
pip freeze > requirements.txt
The file might contain packages such as:
openai
python-dotenv
fastapi
uvicorn
Another developer can install the dependencies using:
pip install -r requirements.txt
Common LLM API Parameters
Different providers offer different options, but common concepts include:
Model
Specifies which AI model processes the request.
model="model-name"
Different models may offer different balances of:
- Capability
- Speed
- Cost
- Context size
- Reasoning ability
Input
The actual prompt or data provided to the model.
input="Explain neural networks."
Instructions
Instructions define how the AI should behave.
instructions="Explain concepts to beginners."
Output Length
Some APIs allow developers to control or limit how much text the model generates.
Limiting unnecessary output can help control:
- Cost
- Latency
- Application behavior
LLM API Costs
Most hosted LLM APIs are paid based on usage.
Pricing commonly depends on factors such as:
- Model selected
- Input tokens
- Output tokens
- Cached input
- Additional tools or capabilities
For example, sending a very large document generally consumes more tokens than sending one short sentence.
A production application should therefore monitor API usage.
How to Reduce LLM API Costs
You can reduce unnecessary API usage in several ways.
Use concise prompts when possible.
Avoid repeatedly sending irrelevant conversation history.
Limit unnecessarily long responses.
Use smaller or cheaper models for simple tasks where appropriate.
Cache results that do not need to be generated repeatedly.
Retrieve only the relevant document sections when using large knowledge bases.
Cost optimization becomes increasingly important when an application has many users.
LLM API Security Best Practices
Security is extremely important when working with AI APIs.
Never expose API keys
Do not put your API key inside:
Flutter application code
React frontend code
JavaScript bundles
GitHub repositories
public HTML
Store it securely on your backend.
Use Environment Variables
Store credentials using environment variables:
OPENAI_API_KEY=...
Then load them from Python.
Validate User Input
If users can send prompts through your application, validate the requests.
For example:
if not prompt:
return {"error": "Prompt is required"}
You may also want to enforce sensible request-size limits.
Add Authentication
If your AI API endpoint should only be available to registered users, protect it with authentication.
Examples include:
- JWT authentication
- Session authentication
- Firebase Authentication
- OAuth
Add Rate Limiting
Without rate limiting, one user could potentially send thousands of requests and create unexpected costs.
For example:
User
↓
1000 requests
↓
Your backend
↓
LLM API
↓
Unexpected bill
Production applications should consider per-user and per-IP request limits.
Common Beginner Mistakes
Hardcoding API Keys
Avoid:
api_key = "secret-key"
Use environment variables instead.
Uploading .env to GitHub
Always add:
.env
to .gitignore.
Sending Every Message Ever Written
Sending unnecessarily large conversation histories increases token usage.
Only send relevant context.
Trusting Every AI Response
LLMs can generate incorrect information.
Important outputs should be validated when necessary.
Ignoring Errors
Do not assume every API request will succeed.
Use proper exception handling.
Calling the LLM Directly from a Mobile App
Secret credentials should generally remain on a trusted backend.
LLM APIs from Different Providers
The basic development pattern is similar across many providers.
For example:
Python Application
↓
Provider SDK
↓
LLM API
↓
Model
↓
Response
The Python syntax differs slightly depending on the provider.
For example, Anthropic currently provides an official Python package installed with:
pip install anthropic
and uses a messages-based interface in its Python SDK.
Therefore, the core concepts you learn in this guide remain useful even when switching providers.
REST API vs Python SDK
There are two common ways to communicate with an LLM API.
Using the Python SDK
Example:
client.responses.create(...)
Advantages include:
- Easier syntax
- Built-in Python types
- Convenient error handling
- Less manual HTTP code
For beginners, using the official SDK is usually easier.
Using the REST API Directly
Python can also make HTTP requests directly using packages such as:
requests
httpx
The application manually sends an HTTP request to the provider’s API endpoint.
This gives developers more control but requires more code.
A provider SDK usually handles much of this work automatically.
LLM API vs Running an LLM Locally
Using an API means the model runs on infrastructure provided by another company.
Your App
↓
Internet
↓
LLM Provider
Running locally means the model runs on your own hardware.
Your App
↓
Local Model
↓
Your Computer or Server
LLM APIs are often easier for beginners because you do not need to manage powerful GPUs or model infrastructure.
Local LLMs can be useful when greater control or particular privacy and deployment requirements matter.
Projects You Can Build with Python and an LLM API
Once you understand basic API calls, you can build practical AI projects such as:
- AI chatbot
- PDF question-answering tool
- AI coding assistant
- Resume analyzer
- Article summarizer
- Email-writing assistant
- Customer-support bot
- AI study assistant
- Product-description generator
- Translation assistant
- Review classifier
- Document-data extractor
- AI search application
A simple chatbot is usually a good first project.
Simple AI Application Workflow
A typical AI application could work like this:
User enters question
↓
Python receives question
↓
Python validates input
↓
Prompt is created
↓
LLM API receives request
↓
Model generates response
↓
Python receives response
↓
Response displayed to user
As the application becomes more advanced, you can add:
Authentication
Database
Conversation history
RAG
Vector database
Tools
Function calling
Streaming
Analytics
Caching
Rate limits
But these are not required for learning the basic LLM API workflow.
Complete Beginner Example
Here is a simple complete example:
import os
from dotenv import load_dotenv
from openai import OpenAI
load_dotenv()
client = OpenAI(
api_key=os.getenv("OPENAI_API_KEY")
)
def ask_llm(question):
try:
response = client.responses.create(
model="gpt-5.5",
instructions="""
You are a helpful AI teacher.
Explain concepts in simple language.
""",
input=question
)
return response.output_text
except Exception as error:
return f"Error: {error}"
while True:
question = input("\nYou: ")
if question.lower() == "exit":
print("Goodbye!")
break
answer = ask_llm(question)
print("\nAI:", answer)
This program:
- Loads your API credentials
- Creates an LLM client
- Accepts user questions
- Sends questions to the model
- Receives generated responses
- Displays the answers
- Continues until the user types
exit
This is enough to understand the basic architecture behind many AI-powered applications.
Frequently Asked Questions
What is an LLM API?
An LLM API allows software applications to communicate with a Large Language Model and receive AI-generated outputs.
Can I use an LLM API with Python?
Yes. Python is one of the most popular languages for integrating LLM APIs.
Do I need machine learning knowledge?
No. You can begin using an LLM API without understanding how to train machine learning models.
Do I need an API key?
Most commercial LLM providers require an API key or another authentication method.
Should I put my API key in Python source code?
No. Store API keys in environment variables or a secure secret-management system.
Can I build a chatbot using Python and an LLM API?
Yes. A chatbot is one of the easiest beginner projects for learning LLM APIs.
Are LLM APIs free?
Some providers may offer limited free usage or credits, while others charge from the beginning. Pricing depends on the provider and model and can change over time.
Can I connect an LLM API to Flutter?
Yes. A common architecture is:
Flutter
↓
Python Backend
↓
LLM API
Keeping the LLM API key on your backend is safer than embedding it in the Flutter application.
Can an LLM API return JSON?
Yes. Many modern LLM APIs can generate structured data, and some providers offer dedicated structured-output capabilities.
What can I build after learning LLM APIs?
You can move on to concepts such as:
Prompt Engineering
↓
LLM APIs
↓
Structured Outputs
↓
Function Calling
↓
Embeddings
↓
Vector Databases
↓
RAG
↓
AI Agents
Final Thoughts
Using an LLM API with Python is one of the easiest ways to begin building real AI-powered applications.
You do not need to train a Large Language Model yourself.
The basic workflow is straightforward:
Create an API account
↓
Get an API key
↓
Install the Python SDK
↓
Store the API key securely
↓
Create the API client
↓
Send a prompt
↓
Receive the model response
↓
Use the response in your application
Start with a simple Python script that sends one question to an LLM.
Once you understand that basic workflow, you can gradually move toward more advanced applications involving conversation history, structured outputs, streaming, document retrieval, function calling, RAG, and AI agents.
The most important thing is to understand the core pattern: your Python application sends input to an LLM API, the model processes it, and your application receives the generated output.
From that simple concept, you can build everything from a small command-line chatbot to a complete AI-powered web or mobile application.
