Learn zero-shot prompting where LLMs perform tasks from natural language instructions alone. Understand when zero-shot works and when to use few-shot instead.

Learn how Chain-of-Thought prompting improves LLM accuracy by 30%+ on complex tasks. Interactive examples with math and logic problems.

Master few-shot prompting by providing 2-8 examples. See how examples improve accuracy on classification, extraction, and formatting tasks with live demos.

Improve accuracy by generating multiple reasoning paths and voting on the most common answer. See how self-consistency boosts reliability on complex tasks.

Learn Tree of Thoughts prompting: branch, evaluate, and backtrack through solution space. Interactive tree visualization shows how LLMs explore alternatives.

Discover meta-prompting: use LLMs to generate, critique, and refine prompts. See how models can self-improve prompt quality through iteration.

Learn how models can check their own answers and fix errors

Break down complex problems into simpler subproblems and solve bottom-up

Use code generation for precise calculations instead of text reasoning

Verify factual claims one by one to reduce hallucinations

Understand RAG: retrieve relevant documents, inject into context, reduce hallucinations. Interactive visualization of vector search and retrieval pipeline.

Master prompt chaining: decompose complex workflows into sequential steps. Learn when to chain vs single prompt, with real-world pipeline examples.

Generate relevant facts first, then use them to provide more accurate answers

Get reliable structured output from LLMs using JSON Schema, function calling, and output parsers. Learn validation and error handling strategies.

Let LLM automatically generate and optimize prompts for your tasks

Automatic selection and use of tools based on task requirements

Chain of thought reasoning for tasks involving images and text

Structure your prompts using proven frameworks

Learn how to combine prompting techniques into powerful recipes for complex real-world tasks