Intelligence Engine Applications & Developer Ecosystems

AI coding workspaces move beyond autocomplete into agents that read a repo, plan edits, run tests, and open pull requests. Cursor …

Test-driven prompting treats prompts like code: write the expected behavior as tests first, then iterate the prompt until it passe…

Free-form model output breaks downstream code. Schema enforcement constrains generation to a typed structure (JSON Schema, Pydanti…

Fine-tuning adapts a base model to your domain, format, or style, but the dataset matters more than the training run. Good dataset…

Prompts are production code that changes behavior, cost, and quality, yet they are often edited in place with no history. Semantic…

AI can generate components, layouts, and even full screens from a prompt or a design. The speed is impressive; the risk is inconsi…

RLAIF uses a model as the judge to score outputs, replacing or augmenting human feedback in RLHF. It scales feedback collection an…

LLM agents can simulate users walking through flows, clicking, and reporting friction before real users do. Synthetic testing find…

AI systems touch copyright, licensing, and liability at every layer: training data, model weights, generated output, and user cont…

Context inflation is the gradual bloating of prompts with history, retrieved documents, and tool results until cost and latency ex…