llm

The top free AI prompt libraries of 2026 — curated collections of tested prompts for ChatGPT, Claude, Gemini, and open models — organized by use case and reviewed for actual quality.

A foundation model is any broadly capable model trained on massive data. An LLM is a specific kind — foundation models also include vision, audio, and multimodal.

Prompt injection is when an attacker hides instructions in user input or external content, hijacking the AI to do something it should not.

Hallucination is when an AI model generates confident but false information. It is the biggest risk in production LLM applications.

The context window is the maximum number of tokens an AI model can read and write at once. Bigger windows let the model handle longer documents and conversations.

A token is the basic unit of text an AI model reads and writes. One token is roughly 3-4 English characters, not a full word.

Temperature is a parameter that controls how random or focused an AI model's output is. Lower values produce predictable text; higher values produce creative text.

ChatGPT explained simply for beginners. Learn how it's built, why it sometimes lies, and how to use it well. No technical background needed.

Fine-tuning explained for beginners. Learn how companies customize general AI models for specific tasks — and when fine-tuning is worth it.

LLMs explained for beginners. Learn what ChatGPT, Claude, and Gemini really are under the hood, and why they work so well.

Fine-tune an open LLM on your domain data with LoRA, QLoRA, or hosted services — without a $100K GPU cluster.

Ship a Perplexity-style AI search engine using embeddings, RAG, and streaming LLM responses — deployed on your own infrastructure.

Fine-tune Llama, Mistral, or Qwen on your custom data using LoRA. Covers dataset prep, training on Runpod/Modal, and deployment via vLLM.

Open-source AI has closed the gap with frontier models. Run Llama 4 locally with Ollama, deploy production endpoints with vLLM, and chat via OpenWebUI — all free.

The major LLM providers compete on context window, reasoning, multimodality, and pricing in 2026. Here is an objective, benchmark-backed comparison.