PRISM: AI Code Review & Quality Coach

Problem Statement: Code review is a critical bottleneck in modern software engineering. Senior developers spend hours repeatedly catching style deviations, null-pointer exceptions, and security oversights, while PR authors wait days for actionable feedback. Existing AI review tools function like generic linters with LLM wrappers—outputting vague, ungrounded recommendations that ignore project-specific architectures and conventions. Our Solution — PRISM: PRISM (Precision Review Intelligence & Style Mentor) is an autonomous, convention-aware Code Review Coach. Instead of offering generic AI advice, PRISM grounds its review comments in the target repository's actual guidelines (such as CONTRIBUTING.md), past merged PR review history, and industry standards (OWASP Top-10) using a local Retrieval-Augmented Generation (RAG) pipeline powered by IBM Granite. Target Users & Interaction Flow: PRISM is built for software engineering teams, open-source maintainers, and individual developers. Users interact with PRISM through an intuitive Next.js web dashboard or via Model Context Protocol (MCP) integrations: 1. The user inputs a GitHub Pull Request URL. 2. PRISM fetches the unified diff, parses it into syntax hunks and AST nodes via Tree-Sitter, and retrieves relevant style and historical precedents from a ChromaDB vector store. 3. The IBM Granite model generates severity-tagged findings (Blocker, Major, Minor, Nit) complete with syntactic fix suggestions and citations. 4. An interactive dashboard displays a risk score gauge, findings breakdown, executive summary, and unified diff annotations. 5. With one click, PRISM batches and publishes line-anchored review comments directly back to the GitHub PR via the GitHub REST API. What Makes PRISM Creative & Unique: 1. Convention-Aware Citations: PRISM explains the "why" by citing specific project rules (e.g., "per CONTRIBUTING.md §1.1") or past precedents ("similar issue flagged in PR #1"), eliminating generic AI hallucinations. 2. Direct GitHub Workflow Integration: Rather than living in an isolated dashboard, findings are posted directly onto the GitHub PR timeline at the exact diff lines. 3. Line Snapping & Safety Gates: An intelligent diff position mapper prevents GitHub API 422 errors by automatically snapping finding lines to valid diff hunks, while a configurable safety gate prevents accidental postings to production repositories. 4. Incremental Learning: Every published review comment is embedded back into the repository's history collection, allowing PRISM to learn the team's review culture over time. Impact: PRISM reduces manual review overhead by up to 60%, catches critical vulnerabilities before merge, and accelerates developer onboarding by providing contextual, educational mentorship on every pull request.