Treat your prompts like production code.

Version, test, A/B compare, and deploy AI prompt templates using a clean, developer-first Git workflow. No more prompt chaos in Notion or hardcoded source files.

returns_agent_v3.git

Live in Production

Commit Log

v3 (active)2m ago

feat: added strict refund instruction

v210m ago

fix: clarify response tone

v11h ago

Initial commit draft

System:

Prompt Editor

Automated ChecksIDLE

Assert Refund Offer

Assert Politeness

Version History — Git Tree (Click to inspect)● Live branch: main

Latest Commitbranch: main

commit 4d9863f—

What we help teams fix.

Eliminate prompt regressions, untracked changes, author chasing, and model pipeline drifts.

01 / Testing & Integrity

Untested Prompt Regressions

You edit a prompt instructions to solve an edge-case, and it silently breaks three others in production.

Assertion Tests PipelineEvaluating...

Assert: Refund offer is includedChecking...

Assert: Politeness formattingChecking...

02 / Versioning & Storage

Document Decay & Fragmentation

Prompts live scattered in Google Docs, Slack notes, and hardcoded in app code. Nobody knows which is active.

📄 Google Doc: Refund Template v2Drifted

💬 Slack: Alice draft snippetForgotten

💻 src/lib/openai.ts (hardcoded string)Untracked

📄 Notion: production-system-promptsOut-of-Sync

03 / Attribution audit

Chasing Prompt Changes

Who modified the system parameters on production? Finding out takes hours of Slack threads.

Slack Search Query 0m elapsed

1. Sarah M. (searching Slack)checking...

2. Dave L. (checking git blame)pending

3. Alex K. (checking local draft)pending

04 / Incident alerts

Silenced Error Regressions

LLM format breakages and parser crashes happen, but slip past because they are buried in cloud logs.

Server stdout streamstdout.log

JSON parser crash (Invalid char)[ALERT]

Empty response (400 Bad Req)[ALERT]

Close the gaps. Before they break your AI.

Professional workflows for prompt engineering. Save commits, run pipeline assertions, and fetch active templates.

Developer SDK & Integration

Decouple prompts from application code. Fetch the active versions dynamically at runtime using our SDKs, CLI, or HTTP endpoints.

import { GFPClient } from '@gitforprompts/sdk';

const gfp = new GFPClient({ apiKey: process.env.GFP_API_KEY });

// Fetch active version of prompt template at runtime
const systemPrompt = await gfp.prompts.getLatest('customer-support');

const response = await openai.chat.completions.create({
  messages: [
    { role: 'system', content: systemPrompt },
    { role: 'user', content: userInput }
  ]
});

gfp-cli

QA & Testing Infrastructure

Deploy with absolute confidence.

Each prompt modification you perform is validated against custom assertion test sets before it goes live. You don't just update prompt text; you grade it.

Natural Language Assertions: Validate output requirements like "Must mention 30-day window" or "Tone must be apologetic".
Dual-Provider Runner: Uses Groq (Llama-3.3-70B) for ultra-low latency test processing with OpenRouter failover routing.
Quality Confidence Grading: Get instant pass/fail ratios and confidence scores for prompt changes.

Assert suite: refund_eval_matrix100% PASSED

Assert refund window check

PASS (96/100)

Assert formal salutations

PASS (100/100)

Model: llama-3.3-70b-versatile (Groq)Latency: 310ms

Platform Security & Architecture

Production-Ready Security Standards

Your prompts contain crucial business rules. We protect your prompt infrastructure with strict auth protocols and server validation.

Clerk Authentication

Protected user account gates using Clerk, supporting developer primary logins via GitHub OAuth and email fallback routes.

Row-Level Owner Security

All Drizzle database interactions are strictly validated at the controller level to ensure no prompt data leakages occur between organizations.

Hashed Key Storage

Developer API keys are generated as random tokens, verified via SHA-256 hashes, and never stored in plain-text format on the server.