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AI for Developers

AI for Developers course — Prowid
Course duration
1 month / 8 lessons
Individual
Per month 160 AZN
Group
Per month 130 AZN
Paying for the full course in a group
15% discount
Installments
50/50
Subscription
from 30 AZN a month
Artificial intelligence is changing the way programmers work. In this course you will learn to work effectively with AI assistants and add LLM capabilities to your own applications.

Enrollment for a new group is open — send a request to reserve your seat

Lessons are taught live in Azerbaijani and Russian.

Enroll in the course Free trial lesson

Or study on your own in your personal account → with a subscription from 30 AZN a month

Course Curriculum

How LLMs work: tokens, context, limitations
Prompt engineering for developers
AI assistants: GitHub Copilot, Cursor, Claude Code
Writing code, refactoring, tests and code review with AI
Reviewing AI-written code: bugs and security
Working with LLM APIs: OpenAI, Anthropic, Gemini
Structured outputs and tool calling
RAG: a chatbot over your own documents
Getting started with AI agents and MCP
Final project: a web application with AI features
How do we teach?

Learning stages

01

Webinar

Students join the lesson live. At the end of each lesson, students are sent a video recording of the class.

02

Homework

Every assignment is based on the topic covered in the webinar. Homework helps you reinforce the material and understand it better.

03

Working with a mentor

The teacher stays in touch with the group both during the course and outside class. Any questions get answered right away.

04

Portfolio

During the course you'll complete several projects on different topics — they'll show employers what you can do.

Self-paced learning

The full curriculum in your personal account

104 topics with theory, examples and practice. The first 4 are free, the rest come with a subscription. Interactive lessons with slides and coding trainers: 94.

Start for free Subscription from 30 AZN a month
How Large Language Models Work9 topics
  1. AI for Developers: What an LLM Is and What You Will Learn
  2. Tokens: How a Model Sees Text
  3. The Context Window: A Model's Working Memory and Its Limits
  4. Training vs Inference: Where a Model's Knowledge Comes From
  5. Text Generation: Next-Token Prediction, Temperature and Top-p
  6. Hallucinations: Why Models Are Confidently Wrong
  7. LLM Limitations: Where Models Consistently Struggle
  8. The Model Landscape: Choosing a Model for the Task
  9. A Map of AI Tools for Developers
Prompt Engineering for Developers10 topics
  1. Anatomy of a Good Prompt: Clear, Specific Instructions
  2. Context in Prompts: Code, Errors, Environment and Delimiters
  3. Examples in Prompts: Zero-Shot and Few-Shot
  4. Roles and System Prompts
  5. Controlling the Output Format: Length, Structure, JSON
  6. Letting the Model Think: Chain of Thought and Reasoning Models
  7. Decomposition: Breaking a Complex Task into Steps
  8. Iterating on Prompts: Diagnosing and Improving
  9. Prompt Templates with Variables
  10. Project: A Prompt Library for Everyday Developer Tasks
AI Coding Assistants: Copilot, Cursor, Claude Code9 topics
  1. GitHub Copilot: Completions, Chat and Agent Mode
  2. Cursor: The AI Editor, Inline Edits and Agent
  3. Claude Code: An Agentic Coding Tool in the Terminal
  4. Claude Code Workflows: Plan, Code, Test, Commit
  5. Project Context for Assistants: CLAUDE.md, AGENTS.md and Rules
  6. Briefing an AI Agent: Specs and Acceptance Criteria
  7. Reviewing AI-Generated Code
  8. Security of AI-Generated Code
  9. Privacy, Secrets and Licensing When Using AI Tools
AI in Everyday Development10 topics
  1. Explaining Unfamiliar Code with AI
  2. Writing Code with AI: From Requirements to a Working Function
  3. Debugging with AI: From Stack Trace to Root Cause
  4. Refactoring with AI Without Breaking Things
  5. Writing Tests with AI
  6. Documentation with AI: Comments, README, Commit Messages
  7. AI as a Reviewer: Code Review for Your Own and Team Code
  8. Learning New Technologies with AI Without Becoming Dependent
  9. Working with Legacy Code: Understand, Document, Change
  10. Project: AI-Assisted Refactoring of a Legacy Project
Working with LLM APIs10 topics
  1. LLM APIs: Accounts, Keys and Environment Variables
  2. Your First Request with an SDK in Python and JavaScript
  3. The Messages Format: Roles, History and the System Prompt
  4. Request Parameters: max_tokens, Temperature, Stop Sequences
  5. Streaming: Showing the Response as It Is Generated
  6. Errors, Retries and Rate Limits
  7. Counting Tokens and Estimating Cost
  8. Prompt Caching
  9. Multiple Providers: Anthropic, OpenAI, Gemini and Compatible APIs
  10. Project: A Command-Line AI Assistant for Developers
Structured Outputs and Tool Calling8 topics
  1. Structured Outputs with JSON Schema
  2. Typed Responses: Pydantic and Zod
  3. Extracting Data from Unstructured Text
  4. Tool Use: How a Model Calls Your Functions
  5. The Tool Use Loop: Executing Tools and Returning Results
  6. Multiple Tools, Parallel Calls and tool_choice
  7. Designing Good Tools
  8. Project: A Support Ticket Triage Service
AI Features in Web Applications9 topics
  1. Architecture of a Web App with an LLM
  2. Streaming to the Browser: Server-Sent Events
  3. Chat UI: Messages, Markdown and States
  4. Conversation History and Context Management in a Chat App
  5. A Text and Document Summarization Feature
  6. Text Classification with LLMs
  7. Moderating User-Generated Content
  8. Translation and Localization with LLMs
  9. Project: A Streaming Web Chatbot
Embeddings, Vector Databases and RAG10 topics
  1. Embeddings: The Meaning of Text as a Vector
  2. Semantic Search Without a Database
  3. Vector Databases and Chroma
  4. pgvector: Vector Search in PostgreSQL
  5. RAG: Giving a Model Knowledge from Your Documents
  6. Loading Documents and Chunking
  7. Retrieval Quality: Hybrid Search, Reranking and Query Rewriting
  8. Grounded Answers: Prompts, Citations and Saying “I Don't Know”
  9. Evaluating RAG: Retrieval Metrics and Answer Accuracy
  10. Project: Document Q&A with RAG
AI Agents7 topics
  1. Workflows and Agents: Patterns for LLM Systems
  2. Building an Agent Loop from Scratch
  3. Agent Planning and Self-Correction
  4. Agent Memory: Context, Notes and Long-Term Storage
  5. Guardrails: Keeping an Agent Under Control
  6. Agent Frameworks and SDKs
  7. Project: An AI Agent with Tools
Model Context Protocol (MCP)6 topics
  1. What Is the Model Context Protocol
  2. Connecting MCP Servers to Claude Code, Cursor and Desktop Apps
  3. Writing an MCP Server in Python
  4. Writing an MCP Server in TypeScript
  5. MCP: Resources, Prompts and Testing a Server
  6. Project: An MCP Server for Your Own Tool
Multimodality and Local Models5 topics
  1. Working with Images: Vision in the API
  2. PDFs and Documents: Extracting Text, Tables and Meaning
  3. Speech to Text: From Audio to Transcripts and Insights
  4. Local Models with Ollama
  5. Ollama in Code: API, OpenAI Compatibility and Local RAG
Quality, Security and Optimization7 topics
  1. Evals: Test Sets for LLM Features
  2. LLM-as-Judge: Using a Model to Grade a Model
  3. Logging, Tracing and Monitoring LLM Applications
  4. Prompt Injection: Attacks and Defenses
  5. Data Leakage and Validating Model Output
  6. Optimizing Cost and Latency
  7. Project: Evals and Security Testing for Your AI App
Responsible AI, Career and Final Project4 topics
  1. Responsible AI: Bias, Transparency and Law
  2. AI and the Job Market: A Developer's Career in the LLM Era
  3. Final Project: Idea, Specification and Architecture
  4. Final Project: A Web Application with AI Features

Group and individual classes include a teacher, homework review and a certificate. The subscription gives you materials, exercises and progress tracking at your own pace, without teacher feedback.

AI for developers course: how to enroll

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