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fomativeh/README.md

Hi, I’m Itive Peace (@fomativeh)

Frontend Engineer · React / Next.js · AI Integration

I build modern, high-performance frontend applications with React and Next.js, and integrate AI features (LLMs, RAG, chat interfaces) into real products. My backend experience helps me ship end-to-end features cleanly, without frontend–backend friction.

What I do

Frontend (Primary)

  • Build production-ready UIs with React & Next.js
  • Create reusable component systems with Tailwind CSS
  • Write type-safe frontend code with TypeScript
  • Implement animations & interactions (GSAP, motion)
  • Optimize performance, UX, and accessibility

AI / LLM Integration

  • Prompt engineering with systematic iteration & testing
  • Maintain prompt libraries and versioned prompts
  • Build RAG chatbots (vector search + context injection)
  • Integrate AI into Next.js + Supabase (or other DBs)
  • Design chat UIs and AI-powered workflows

Backend (Supporting)

  • Node.js, Express APIs
  • Telegram bots, automations, scrapers
  • Auth, webhooks, background jobs
  • Database design & API integration

Tech Stack

Frontend

  • React, Next.js
  • TypeScript, JavaScript (ES6+)
  • Tailwind CSS, Sass
  • Redux
  • GSAP

AI / Data

  • LLM APIs
  • RAG pipelines
  • Vector databases
  • Supabase (Postgres)

Backend & Tooling

  • Node.js, Express
  • PostgreSQL, MongoDB, Redis
  • Docker, Nginx
  • AWS, Firebase
  • Git, GitHub
  • Jest, Cypress

Contact

Online

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  1. RAG-Chatbot RAG-Chatbot Public

    🤖 Intelligent RAG chatbot with dual modes: COVID-19 medical assistant + custom document Q&A. Built with LangChain, ChromaDB & Streamlit. Upload PDFs -> Ask questions -> Get cited answers instantly

    Python

  2. prompt-library prompt-library Public

    A collection of AI prompts I've built for real business use cases. Covers content creation, data analysis, coding help, customer support, education, and professional writing. Each prompt includes e…

    5 1

  3. prompt-eval-harness prompt-eval-harness Public

    Python harness for evaluating and A/B testing LLM prompt variants against a labeled dataset, with deterministic scoring and regression checks.

    Python

  4. auto-claim-rag auto-claim-rag Public

    Auto insurance claim document extraction using a small RAG knowledge base plus Pydantic-validated structured output, with a lightweight eval harness.

    Python