PearAI

Start with the official PearAI product or documentation and choose one bounded task matching its core purpose: AI code editor and development environment combining chat, code generation, context, and agentic coding workflows. Use a realistic question, repository, interface brief, reference image, or media requirement so you can evaluate the actual workflow rather than a generic demo.

Provide only the context needed for the task. For coding agents, work in a branch or disposable environment and review permissions before allowing commands. For search tools, retain source links. For visual generation, specify dimensions, style, references, and intended usage. For APIs, keep credentials server-side and add request timeouts, retries, and usage tracking.

Review several outputs and difficult cases. Check factual grounding, code diffs, test results, visual consistency, artifacts, latency, and failure behavior. Generated code should pass your normal review and CI process; generated media should be inspected for rights, brand suitability, and unwanted details before publication.

Before scaling, confirm privacy, retention, rate limits, licensing, export formats, observability, and expected cost. The catalog currently classifies pricing as freemium, but current billing and plan limits should be verified directly with the provider.