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Switch courseAI for developers course

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

AI for developers course · Working with LLM APIs

Prompt Caching

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