A newsroom with 20 years of archived reporting holds an invaluable corpus of verified, sourced, expert-reviewed content that no external AI tool can access. Converting this archive into a RAG-ready knowledge base transforms it from a passive historical record into an active AI-research asset — one that can answer journalist queries with verified archive citations in seconds.
The Four-Step Archive-to-RAG Pipeline
Step 1: Export and clean. Export all articles from your CMS as structured JSON or XML. Clean out boilerplate (navigation, ads, cookie notices), standardise metadata (date, author, section, tags). Step 2: Chunk and embed. Split articles into 500–1000 token chunks. Generate vector embeddings for each chunk using an embedding model (OpenAI text-embedding-3-small, Cohere embed-v3). Step 3: Index in a vector database. Store chunks and embeddings in a vector database (Pinecone, Weaviate, Chroma). This enables semantic search: query "What did we report about [Topic] in 2022?" and retrieve the 10 most semantically relevant archive chunks. Step 4: Build the query interface. Connect the vector database to an LLM (GPT-4o, Claude) to generate answers with archive citations. Journalists query the system in natural language; it returns synthesised answers with article-level citations from the archive.