How can I increase the number of sources used by you, ChatGPT?
Executive summary
Increasing the number of sources ChatGPT draws from requires two parallel moves: supply more indexed, retrievable content (via uploads, connectors, or a RAG/knowledge platform) and configure the model or workspace to prefer those sources when answering; OpenAI’s “company knowledge” and app-sync features plus third‑party knowledge-base tools are the practical levers to do both [1] [2] eesel.ai/blog/chatgpt-knowledge-base" target="_blank" rel="noopener noreferrer">[3].
1. Add and sync authoritative repositories so the model can actually see more sources
The simplest structural change is to connect or sync the places where your knowledge already lives—Slack, Google Drive, SharePoint, GitHub and similar—so ChatGPT can search them when composing answers; OpenAI’s company knowledge feature specifically searches connected apps and surfaces citations from those apps [1] [4], and the Help Center says indexing selected sources speeds up and improves answers [5] [2].
2. Use knowledge uploads and GPT “Knowledge” to seed domain content
Where connectors aren’t available, upload files or add knowledge to a custom GPT: builders can attach documents to a GPT so it will rely on that Knowledge first, and the GPT editor can be instructed to cite uploaded files if desired [6] [7]. The developer community and Help Center both show that “feeding” a GPT with domain docs or code makes the model prefer those chunks for Q&A and summarization tasks [8] [6].
3. Employ Retrieval‑Augmented Generation platforms to broaden source coverage
Rather than changing the base model, many workflows use RAG or a dedicated knowledge‑base product that indexes dozens or hundreds of sources and then supplies relevant passages at query time; practitioners recommend RAG because it forces answers to be grounded in provided documents and reduces hallucinations, and vendors like eesel promote many one‑click integrations to keep that index fresh [3].
4. Adjust permissions, admin settings, and indexing to maximize accessible sources
More sources won’t help if the workspace blocks them: admin control and data‑residency settings determine which connectors are visible, and initial syncing/indexing is required before content is usable [5] [2]. The OpenAI docs stress that ChatGPT only accesses what a user already has permission to view, so expanding the model’s sources often means changing who can connect which apps in a Business/Enterprise workspace [4].
5. Combine technical feeding with prompt and GPT‑builder instructions to favor multiple citations
Technical connection is necessary but not sufficient; instructing a GPT to “rely on Knowledge first” or explicitly requesting multiple sources in the prompt increases the chance the reply cites several references, and company knowledge will show which sources it used and the snippets it pulled so answers can be verified [6] [1].
6. Know the platform limits and where to expect diminishing returns
A core limitation remains: the pretrained model’s internal training data can’t be rewritten by end users, so adding external sources augments retrieval but does not change what the base model “knows” from training [9]. For domain precision, prioritize indexed, high‑quality documents and RAG-style retrieval rather than relying on the base model to recall obscure facts unaided [10] [3].
7. Practical checklist to increase the number of sources used
Connect and sync as many eligible apps as admins permit, upload targeted documents into GPT Knowledge, deploy a RAG/indexing layer or a knowledge‑platform integration, set GPT Instructions to prefer Knowledge and request citations, and confirm indexing is complete so the model can surface multiple sources—these steps are explicitly supported across OpenAI help docs and third‑party guides [1] [5] [3] [6].