Mission: Build a RAG System

Retrieval-augmented generation transforms your documents and knowledge base into an intelligent system that answers questions with accuracy and source citations. This mission deploys your AI squad to build a complete RAG pipeline with document ingestion for PDFs, Word docs, web pages, Notion exports, and Markdown, vector database setup, a retrieval and ranking system, LLM integration, and an admin interface for managing the knowledge base. Forge implements proper document chunking, embedding generation, and hybrid search combining vector similarity with keyword matching for optimal retrieval accuracy. The squad achieves 85-95% accuracy on domain-specific questions by combining smart chunking strategies, retrieval reranking, and carefully engineered prompts with source citations so users can verify every answer. ShipSquad RAG pipelines differ from basic vector search implementations because we optimize every stage of the pipeline: chunk sizing, overlap, embedding model selection, retrieval strategy, and prompt engineering. We select the best vector database for your needs, whether Pinecone for managed simplicity, Weaviate for hybrid search, or Chroma for rapid prototyping. The mission delivers in 2-4 weeks with an admin interface for adding, updating, and removing knowledge base documents.