Having 5 years of experience across Business Analysis and Quality Assurance in the ERP domain, working on AI-enabled document processing systems.

My work focuses on bridging business requirements with AI and engineering teams to deliver reliable, scalable GenAI solutions. I have hands-on experience with LLM-based systems, including entity extraction, document classification, OCR, and prompt engineering, and exposure to modern AI architectures such as Agentic AI, Retrieval-Augmented Generation (RAG), Vector Databases, and MCP-based workflows.

Currently, I’m transitioning toward AI Product Management, supported by formal training through HelloPM and practical experience on GenAI products.

• 📌 Defining product requirements, user stories, and acceptance criteria for AI-enabled features
• 🤖 Improving LLM-based entity extraction & classification using ground truth, prompt design, and model evaluation
• 📊 Analyzing extraction data to identify error patterns, edge cases, and improvement opportunities
• 🧪 Validating GenAI model accuracy, performance, and reliability
• 🔁 Supporting Agile delivery, backlog prioritization, UAT, and release readiness
• 📄 Maintaining product and prompt engineering best-practice documentation

• AI Product Management (HelloPM)
• Product strategy, roadmaps, and prioritization for AI products
• AI success metrics, evaluation frameworks, and trade-offs
• Prototyping and AI-first product thinking

• Product strategy & roadmap fundamentals
• Feature prioritization & problem framing
• Requirement gathering, user stories, acceptance criteria
• Stakeholder management & UAT
• Product & process documentation

• LLM fundamentals & prompt engineering
• Agentic AI concepts & workflow orchestration
• Retrieval-Augmented Generation (RAG) systems
• Vector Databases (embedding-based retrieval)
• MCP-based AI workflows
• Entity extraction & document classification
• Ground-truth creation & model evaluation
• AI quality metrics, accuracy validation, and reliability testing

• Python, SQL (PostgreSQL), Pandas, NumPy
• Web & API testing (Postman, pytest)
• Performance testing (JMeter)
• Defect tracking (JIRA, Zephyr, TestLink)

• JIRA, Figma, Postman, JMeter
• VS Code, PyCharm, Git
• Cursor, Amazon Q, Lovable (AI productivity tools)
• AWS (S3, EC2)
• Vector DB–backed retrieval systems (RAG pipelines)

• Web Automation Framework
  A scalable Playwright + Pytest framework for modern web automation.

• API Automation Framework
  Pytest-based framework for REST API testing and validation.

• Text Comparator
  Web tool to compare text outputs with visual highlights for differences — useful for validating AI extraction results.

AI Product Management (HelloPM) — Jan 2026

• Product strategy & roadmap planning
• Prioritization frameworks
• AI product thinking & success metrics
• Prompt engineering fundamentals

I’m open to AI Product, Business Analyst, and GenAI-focused roles and collaborations.

• 🔗 LinkedIn
• 📧 Email: prashantf18@gmail.com
• 💬 WhatsApp: +91 9130902721

Thanks for visiting my GitHub — happy to connect and collaborate!