Prompting Techniques

Master advanced prompting methods for better AI responses

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.

Chain-of-Thought — Step-by-Step AI Reasoning

Think step by step

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

Few-Shot Learning

Learning from examples

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

Self-Consistency — Improving Accuracy via Voting

Vote for the best answer

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

Tree of Thoughts

Branching reasoning

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

Meta-Prompting

Prompts that improve prompts

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

Reflexion — Self-Correcting LLM Outputs

Self-correction through reflection

Learn how models can check their own answers and fix errors

Least-to-Most — Bottom-Up Problem Decomposition

From simple to complex

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

Program of Thought

Code instead of text

Use code generation for precise calculations instead of text reasoning

Chain of Verification

Fact-checking each claim

Verify factual claims one by one to reduce hallucinations

RAG — Retrieval-Augmented Generation

Retrieval Augmented Generation

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

Prompt Chaining

Sequential task decomposition

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

Generate Knowledge

Think before answering

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

Structured Output

JSON, XML, YAML

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

APE — Automatic Prompt Engineering

Automatic Prompt Engineer

Let LLM automatically generate and optimize prompts for your tasks