AI Playground
Explore AI, LLMs, RAG, Agents, and MCP through interactive visual simulations. Learn concepts, investigate system parameters, and solve engineering challenges.
1. AI Fundamentals
Explore the absolute foundations of Machine Learning, Neural Networks, and Generative LLMs.
AI Knowledge Quiz
Test your knowledge of core AI, ML, Deep Learning, and Generative LLM architectures.
2. LLM Mechanics
Visualize tokenize streams, investigate context budgets, and adjust deep inference parameters.
Token Visualizer
Analyze how text inputs decompose into numerical token structures and verify cost ratios.
Context Window Lab
Manage prompts, systemic instructions, and documents in a live, visual context window layout.
LLM Visualizer
Trace the exact network path from user prompts, context compiling, and next-token probability prediction.
LLM Parameters Lab
Fine-tune inference temperatures, Top-K, Top-P, and max-token thresholds inside response simulators.
Embedding Spaces
Plot text meanings inside semantic 2D vector coordinate frames and resolve cosine similarities.
3. Retrieval-Augmented Generation
Augment raw model prompts by retrieving verified, semantic knowledge chunks from vector databases.
RAG Simulator
Audit chunk retrieval, check vector DB searches, and construct context payloads.
RAG Detective
Inspect raw database chunks, evaluate semantic overlaps, and find the perfect reference context.
4. Agentic AI & Tooling
Orchestrate autonomous agent planning loops and connect LLMs directly to browser execution tools.
Tool Calling Simulator
Model the precise cycle where LLMs decide to parse function schemas and execute external tools.
Agentic Loop Simulator
Orchestrate autonomous agent loops: monitor goals, analyze plans, execute tools, and log observations.
5. Model Context Protocol
Standardize communication pipelines between client assistants and target data servers.
MCP Concept Explorer
Investigate the unified MCP spec. Map Client-Server links, Tool resources, and Prompt templates.
6. AI Engineering
Integrate AI nodes into production software: format outputs, stream bytes, and audit model architectures.
Prompt Engineering Lab
Improve bad prompting statements into descriptive, contextual templates with constraint boundaries.
Structured Output Lab
Force LLMs to return strict, parseable JSON files instead of standard descriptive sentences.
Streaming Visualizer
Verify progressive response streaming. Contrast latency improvements of TTFT (Time-To-First-Token).
AI Evaluation Suite
Evaluate and benchmark models based on multi-dimensional quality, cost, and speed trade-offs.
Observability Tracker
Trace nested LLM execution chains, check model latency graphs, and log token costs.
AI Architecture Builder
Connect frontend layouts, caching layers, and vector databases into secure, highly-available architectures.
7. AI Security
Defend model integration pipelines against injection strings, parameter leaks, and hijacked tool scopes.
AI Security Detective
Identify prompt injections, excessive agencies, insecure tool integrations, and data poison leaks.