Tutorials

Master AI development with our comprehensive tutorials. From your first API call to production-ready applications.

Learning Paths

Structured courses to take you from beginner to expert. Complete each path to earn a certification.

🌱 Beginner

AI Fundamentals

Start here to learn the basics of AI APIs

📚 8 lessons ⏱️ 4 hours
  • 1Introduction to AI APIs
  • 2Your First API Call
  • 3Understanding Tokens
  • 4Basic Prompt Engineering
Start Learning
🚀 Intermediate

Building Applications

Create real-world AI-powered applications

📚 12 lessons ⏱️ 8 hours
  • 1Project Architecture
  • 2Streaming Responses
  • 3Error Handling
  • 4Building Chatbots
Start Learning
🔥 Advanced

Production Systems

Scale AI to enterprise-grade deployments

📚 16 lessons ⏱️ 12 hours
  • 1RAG Architectures
  • 2Autonomous Agents
  • 3Fine-tuning Models
  • 4Production Monitoring
Start Learning

All Tutorials

🚀

Getting Started with MythicDot.AI

Learn the basics: API keys, authentication, and making your first request.

Beginner Python JavaScript
15 min Start →
💬

Building a Conversational Chatbot

Create a chatbot with memory that maintains context across conversations.

Intermediate Python
45 min Start →
📄

Document Q&A with Embeddings

Build a system that answers questions about uploaded documents using vector search.

Intermediate Python
60 min Start →
🌊

Streaming Responses in Real-Time

Implement streaming to display AI responses as they're generated.

Beginner JavaScript
20 min Start →
🤖

Building Autonomous AI Agents

Create agents that can plan, reason, and execute multi-step tasks.

Advanced Python
90 min Start →

Code Examples

Ready-to-run code snippets for common use cases. Copy, paste, and customize.

const response = await mythic.chat({ model: "mythic-1", messages: [{ role: "user", content: prompt }] });

Basic Chat Completion

Simple request/response pattern

📋 Copy ▶ Run
const stream = await mythic.chat({ model: "mythic-1", messages: [...], stream: true }); for await (const chunk of stream) { process(chunk); }

Streaming Response

Real-time streaming output

📋 Copy ▶ Run
const embedding = await mythic.embed({ model: "mythic-embed", input: "Your text here" }); // 1536-dimensional vector console.log(embedding.data);

Generate Embeddings

Vector embeddings for search

📋 Copy ▶ Run

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