AI for Backend Developers

AI fundamentals, APIs, RAG, pipelines, safety, and agent concepts for backend work.

• What is AI? — Artificial intelligence as pattern learning and problem solving.
• Machine Learning vs Deep Learning vs Generative AI — How common AI terms relate to each other.
• How ChatGPT works — A high-level explanation of language model behavior.
• Tokens — The units AI models read, write, and charge for.
• Context Window — How much information a model can consider at once.
• Temperature — How randomness affects model output.
• Hallucinations — Why models can produce plausible but wrong answers.
• Prompt Engineering — Writing instructions that produce useful model behavior.
• Prompt Templates — Reusable prompts for backend workflows.
• Chat Completion — Sending messages to a model and reading responses.
• Structured Output — Getting predictable JSON-shaped model responses.
• Function Calling / Tool Use — Letting models request typed actions from your backend.
• Streaming Responses — Sending partial model output to users as it arrives.
• Token Usage & Cost Calculation — Estimating AI feature cost from token usage.
• RAG — Retrieval augmented generation.
• Embeddings — Representing meaning as vectors.
• Vector Databases — Storing and searching vectors for semantic retrieval.
• Chunking for RAG — Splitting documents for better retrieval.
• Semantic Search vs Keyword Search — Meaning-based search compared with exact matching.
• AI Pipelines — Backend workflows that prepare, call, and post-process AI output.
• API Keys & Authentication — Authenticating safely with AI APIs.
• Caching for LLM responses — Caching model responses where it is correct and useful.
• Rate Limiting — Protecting AI endpoints from overuse.
• AI Security — Prompt injection, data leakage, and defensive backend design.
• What is an AI Agent? — The basic agent loop and how it differs from one-shot prompting.
• Agent Planning & Reasoning — How agents break work into steps.
• Agent Memory — Short-term and long-term memory in agent systems.
• Conversation Memory — Remembering useful context across turns.
• Tools / Function Calling in Agents — How agents use tools to affect external systems.
• Multi-Agent Systems — Multiple agents coordinating on a task.
• Agentic Workflows — Workflow patterns that combine model decisions and deterministic code.