Answers grounded in your knowledge.
Attach RAG datasources to any AI Agent or LLM node so output is grounded in your private knowledge base — cited from your own documents, accurate, and on-brand.
One source of truth, everywhere.
Grounded retrieval
RAG datasources feed agents the right passages from your knowledge base at query time.
Cited, not hallucinated
Answers reference the source titles they came from — restricted to your documentation.
Bring your documents
Upload PDF, DOCX, TXT, MD, PPTX, CSV, XLSX, JSON, and XML.
Composable
Attach the same datasource to AI Agent or LLM nodes across any workflow.
On-brand output
Generations stay anchored to your voice, facts, and guidelines.
Observable
Every run is logged, traceable, and auditable.
Retrieve first, then answer.
Attach a RAG datasource to an AI Agent or LLM node and it searches your knowledge base for the most relevant passages before it answers — so responses are anchored in your documents instead of the model's guesswork. Pair it with a system message that restricts answers to the provided sources.
The formats your knowledge already lives in.
Upload the files your team works with every day. Structured and unstructured documents are accepted as datasources and file inputs across the platform.
Wire a datasource into agents and LLMs.
Select your datasource in the node settings — for example a 'Product docs' source on a support agent. The agent searches it on its own as it reasons, and you can keep external tools off so it relies on RAG only.
Trustworthy, source-backed output.
Grounded support Q&A
Answer customer questions from your docs, citing the source titles used.
On-brand content
Generate copy anchored to a brand brief so tone and facts stay consistent.
Document analysis
Summarize and reason over uploaded PDFs, decks, and spreadsheets.
Policy-safe answers
Restrict the model to provided sources; it says 'I don't know' when absent.
Grounded customer Q&A, end to end.
The agent searches your knowledge base before answering, returns a concise reply with the 1-3 source titles it used, and says it doesn't know when the answer isn't in your sources.
How answers get grounded.
Upload your knowledge
Add PDFs, DOCX, and more as a RAG datasource — no code required.
Attach to a node
Select the datasource on an AI Agent or LLM node in Studio.
Run grounded
The node retrieves the right passages and answers from your sources.
Review & trust
Check the cited source titles; every run is logged for traceability.
What teams ask before they commit.
What is RAG grounding?
RAG — retrieval-augmented generation — means the AI searches a knowledge base for the most relevant passages before it answers, so the response is anchored in real documents instead of the model's guesswork. In Draft & Goal, you attach a RAG datasource to an AI Agent or LLM node and it retrieves first, then answers.
How does Draft & Goal reduce AI hallucinations?
By grounding output in your own documents. A RAG datasource feeds the node the right passages from your knowledge base at query time, answers reference the source titles they came from, and a system message can restrict responses to the provided sources — so the model says it does not know when the answer is not in your documents.
What file formats can I upload as a knowledge base?
Draft & Goal accepts the formats your knowledge already lives in: PDF, DOCX, TXT, Markdown, PPTX, CSV, XLSX, JSON, and XML. Structured and unstructured documents are accepted as RAG datasources and as file inputs across the platform, with no code required to upload them.
Can I reuse the same knowledge base across workflows?
Yes. A datasource is composable: you attach the same one to AI Agent or LLM nodes in any workflow — for example a 'Product docs' source on a support agent. The agent searches it on its own as it reasons, and you can keep external tools off so it relies on RAG only.
Does grounding keep generated content on-brand?
Yes. Because generation is anchored to your documents, output stays consistent with your voice, facts, and guidelines. Teams use Draft & Goal to generate copy grounded in a brand brief, answer customer questions from their docs with cited source titles, and keep every run logged and traceable for review.
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