Prompt Runtime Layer
Route prompts through a single endpoint wrapper that targets OpenAI-compatible providers via OPENAI_BASE_ENDPOINT.
Product
Purpose-built for prompt engineering loops
PromptWrap combines prompt execution, response grading, and trend analytics in one developer-first workflow. It is designed for teams that need measurable feedback while iterating AI UX and reliability.
Instead of jumping between notebooks, logs, and ad-hoc notes, PromptWrap keeps each run structured with prompt text, model output, token estimate, quality score, and rationale.
Quality Evaluation
Score each response from 0-100 with an AI evaluator prompt. When providers fail, fallback heuristics keep QA loops active.
Analytics Snapshot
Track quality and token trends in real time using lightweight client-side charts, with no separate analytics backend.
Demo Prompt
Live Sample Prompt Generator
Loading sample prompt...
A new sample prompt is selected on each page refresh and can be rotated manually.
Workflow
How teams use PromptWrap in practice
Step 1
Submit Prompt
Send a test prompt from the dashboard form using your team prompt pattern.
Step 2
Run Model + Evaluate
PromptWrap generates an answer, estimates token usage, and grades output quality with rationale for review.
Step 3
Analyze Trends
Inspect quality and token charts to catch regressions, measure improvements, and tune prompts faster.
Technical Profile
- - Next.js 14 App Router with TypeScript and Tailwind.
- - OpenAI-compatible runtime call with configurable endpoint.
- - In-memory prompt history for low-friction beta evaluation.
- - Client-side charts for quality score and token trend tracking.
Best Fit Teams
- - AI product teams iterating prompt UX before adding heavy infra.
- - Engineering leads validating output quality in release candidates.
- - Prompt designers building repeatable scoring and review loops.
- - BETA teams needing a deployable analytics shell on Vercel.