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An AI image generation tool that excels at generating images with accurate, legible text embedded in them — a capability that most other image generators struggle with. Ideogram is widely used for creating social media graphics, posters, logos, and marketing materials where text must appear correctly within the image.

The process of running a trained AI model to generate outputs (predictions, text, images) from new inputs. Inference is what happens every time you send a message to ChatGPT — the model uses its trained parameters to generate a response. Inference speed and cost are key factors in AI deployment.

The computational expense of running an AI model to generate outputs (as opposed to the cost of training the model). Inference cost is the primary ongoing expense of deploying AI in production. It is measured in cost per token, cost per image, or cost per API call. Reducing inference cost through model optimization, quantization, and caching is a major focus of AI engineering.

A fine-tuning technique where a pre-trained language model is trained on a large set of instruction-response pairs to make it better at following natural language instructions. Instruction tuning is what transforms a raw language model (which just predicts the next word) into a helpful assistant (which follows directions). Most modern chat AI models, including ChatGPT and Claude, use instruction tuning.

The connection between two or more software systems that allows them to share data and trigger actions. AI integrations connect AI models to business tools like CRMs, email platforms, calendars, and databases. Tools like Zapier, Make, and n8n specialize in creating no-code AI integrations.