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Retrieval-augmented generation (RAG) answers questions using passages retrieved from your documents. Citation-backed RAG goes further: responses link to source pages, chunks, or records so reviewers can verify claims. PaperIQ.ai includes agent chat with source panels and quality metrics—positioned as a trust layer on top of processed tenant documents, not a replacement for schema-validated extraction when you need database rows.
A generic chatbot might say “The lease expires in 2028” without evidence. In regulated or financial workflows, ungrounded answers create liability. Citations let a human click through to the clause, table, or paragraph that supported the answer. PaperIQ separates two jobs: • **Extraction** → schema-valid JSON for systems • **Q&A** → conversational access with citations for investigation and audit Teams need both when operators ask ad-hoc questions but accountants still require structured posting.
RAG is only as trustworthy as its isolation model. PaperIQ emphasizes multi-tenant separation: retrieval and MCP tools operate within tenant boundaries configured by administrators. Combine role-scoped document access (prompt templates tagged to user roles) with citations so users see sources only for documents they are permitted to view.
Features marketed on PaperIQ include hybrid PDF viewing alongside extracted structure—helpful when a citation points to a table region or signature block. The goal is to reduce “trust me” AI moments in ops reviews. Eval metrics and feedback loops (thumbs up/down on responses) help teams track quality over time rather than treating chat as a one-off demo.
If users only need exploratory Q&A and no downstream system load, RAG with citations may suffice temporarily. The moment you export to ERP, CRM, or billing, schema-validated extraction becomes the primary path. Avoid duplicating conflicting truths: structured records should be authoritative; chat should cite those records or source PDFs consistently.
Citations do not guarantee correctness—the source passage may be misread. For high-stakes fields (amounts, dates, legal names), prefer schema validation and human exception queues over chat-only workflows. PaperIQ does not claim hallucination-free chat; it provides grounding tools and separate extraction validation for production data paths.
1. Ingest and process representative documents. 2. Enable agent chat with citations for pilot users. 3. Track which questions repeat—candidate fields for JSON Schema extraction. 4. Automate high-volume fields; keep RAG for edge questions. Related: JSON Schema for Real-World Documents and MCP for Business Data Automation.
Does PaperIQ RAG replace extraction?
No. RAG answers questions over processed content; extraction produces validated JSON for systems. Most production workflows use both.
Are citations page-level or chunk-level?
PaperIQ surfaces source references tied to processed document structure—exact granularity depends on job configuration and document type. Review in the agent UI during pilot.
Can agents call MCP tools during chat?
Yes, where configured. Tenant-registered MCP servers extend chat from retrieval-only to approved actions—subject to your security review.