Vidensbase
Intelligence Engine Applications & Developer Ecosystems
Build, govern, and ship AI-powered developer tools and applications, from coding agents and prompt pipelines to fine-tuning, testing, and compliance.
Denne detaljerede guide findes på engelsk.
10 emner · 10 guider
- Cursor and Devin AI Workspaces
AI coding workspaces move beyond autocomplete into agents that read a repo, plan edits, run tests, and open pull requests. Cursor …
- Test-Driven Prompting
Test-driven prompting treats prompts like code: write the expected behavior as tests first, then iterate the prompt until it passe…
- Schema Enforcement for AI Output
Free-form model output breaks downstream code. Schema enforcement constrains generation to a typed structure (JSON Schema, Pydanti…
- Custom Fine-Tuning Datasets
Fine-tuning adapts a base model to your domain, format, or style, but the dataset matters more than the training run. Good dataset…
- Semantic Versioning for Prompts
Prompts are production code that changes behavior, cost, and quality, yet they are often edited in place with no history. Semantic…
- AI-Driven UI Generation
AI can generate components, layouts, and even full screens from a prompt or a design. The speed is impressive; the risk is inconsi…
- RLAIF: Reinforcement Learning from AI Feedback
RLAIF uses a model as the judge to score outputs, replacing or augmenting human feedback in RLHF. It scales feedback collection an…
- Synthetic User Testing with LLMs
LLM agents can simulate users walking through flows, clicking, and reporting friction before real users do. Synthetic testing find…
- Legal and Copyright Engineering for AI
AI systems touch copyright, licensing, and liability at every layer: training data, model weights, generated output, and user cont…
- Context Inflation Controls
Context inflation is the gradual bloating of prompts with history, retrieved documents, and tool results until cost and latency ex…
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