Post by Nirjhar Sarkar

Stop building simple LLM wrappers. Build systems that scale product thinking. I just finished building an AI PRD & spec generator designed to streamline product definition, prioritization, and edge-case discovery. Instead of a generic chatbot, this system implements production-grade LLM patterns to act as a force multiplier for product managers: What it does: 1. Structured PRD Drafting: Translates rough feature ideas and raw customer feedback into detailed, schema-enforced specifications. 2. Multi-Model RICE Scoring: Generates objective reach, impact, confidence, and Effort scores, forcing the model to explain its reasoning. 3. Automated Edge-Case Checklist: Identifies potential integration failures, permission gaps, and billing corner cases based on the feature type. The Architecture & Tech Stack: - Multi-Model Orchestration: Runs OpenAI (GPT-4o), Anthropic (Claude), and Google (Gemini) through a unified provider adapter. Supports running ensembles in parallel or routing tasks to specific models. - Native Structured Outputs: Enforces strict Zod schemas across all three API families using native capabilities to ensure 100% parseable JSON. - RAG with pgvector: Grounding RICE scores and spec structure in historical team precedents stored in PostgreSQL. - LLM-as-Judge Eval Loop: Employs cross-family evaluation to score PRD drafts on clarity, grounding, and depth. - PM Edit-Ratio Flywheel: Logs and tracks the "edit ratio" over time to measure AI performance gains. - Integrations: Direct export endpoints to push finalized specs to Notion and action items to Jira. - CLI Eval Runner: Running eval cases on demand to prevent regression during prompt updates. This project was a great exercise in orchestrating multiple foundation models, designing deterministic data contracts, and closing the LLM feedback loop. Check out the app: https://lnkd.in/dxcs_QDt #ArtificialIntelligence #ProductManagement #LLMOps #SoftwareEngineering #SystemDesign #OpenSource