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10 RAG Projects to Master Retrieval Augmented Generation in 2026

AI · · 1 min read · x.com ↗

10 RAG Projects to Master in 2026

Author: Suraj Sharma (@suraj_sharma14)
Source: X/Twitter
Date: 2026-05-13

Original Content

  1. PDF Chat Application — Upload PDFs, ask questions, retrieve context-aware answers with citations and semantic search.
  2. AI Research Assistant — Search across documents, summarize findings, generate references, compare sources.
  3. YouTube Q&A Engine — Convert videos into searchable knowledge bases with transcript retrieval and timestamp citations.
  4. AI Customer Support Bot — Connect company docs, FAQs, support history with contextual responses and memory.
  5. GitHub Codebase Assistant — Ask questions across repositories, retrieve relevant code snippets, explain architecture.
  6. Multi-Modal RAG System — Combine PDFs, images, audio, databases into one unified retrieval pipeline.
  7. Personal Knowledge OS — Build your own second brain with notes, bookmarks, search, and AI memory retrieval.
  8. Agentic RAG Workflow — Create autonomous agents that search, retrieve, reason, and execute multi-step tasks.
  9. Enterprise Search Engine — Slack, Notion, Google Drive, Confluence retrieval with permission-aware search systems.
  10. Medical/Legal RAG Assistant — Domain-specific retrieval with hallucination reduction, citations, and verification pipelines.

Summary

List of 10 RAG project ideas ranging from basic (PDF chat) to advanced (agentic workflows, multi-modal systems). Project 7 (Personal Knowledge OS / second brain) is directly relevant to ObsidianBrain concept. Project 5 (GitHub Codebase Assistant) relates to developer tooling workflows.

Metrics

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  • Comments: 4
  • Views: 13.5K

Relevance to FACorreia Projects

MODERATE RELEVANCE — Project 7 (Personal Knowledge OS) maps directly to ObsidianBrain second brain concept. Project 8 (Agentic RAG) maps to Hermes autonomous agent patterns. Project 5 (GitHub Codebase Assistant) could inform Norviq's developer feature set. Most of this is generic AI content — the overlap with your projects is noteworthy but not actionable as inspiration.