An example prompt for an AI document chatbot

Build a support chatbot that answers from a software product's help articles. It needs: conversations and messages, model gateway client and metering, token and usd budgets (fail closed), document ingestion pipeline, pgvector store with tenant isolation and hybrid retrieval and reranking. Rule that must always hold: every call atomically reserves estimated tokens and USD against both the user and org counters; no reservation means no call; settlement records actual usage and a reaper releases abandoned reservations; defaults apply when no limit is configured. Leave out for now: no direct provider SDK calls from app code; every call goes through the metering gateway and no autonomous agent loops or side-effecting tools in phase 1 (max_steps = 1, read-only).

Replace the first sentence with your own idea. The list of needs stays useful whatever the niche, because it comes from what an AI document chatbot has to do.

• Build a policy assistant over a company's HR handbook. Start with conversations and messages and model gateway client and metering, and add token and usd budgets (fail closed) once those work.
• Build a research assistant over a law firm's case notes. People should be able to turn uploaded files and pasted text into searchable, embedded chunks reliably and cheaply.

Forge AI picks a reference architecture from how you describe the app. These phrases point to an AI document chatbot: chatgpt, chat with your documents, chat with pdf, chat with your data, rag, retrieval augmented generation, ai chatbot and ai assistant.

• Reserve-then-settle budgets that fail closed before the provider call.
• Hybrid retrieval with reciprocal rank fusion and a grounding gate.
• Queued, idempotent, content-hashed ingestion.
• Treat retrieved and uploaded text as hostile, and keep phase 1 read-only.

This is a reference design for the category, not a promise that every item is generated today. Built-ins such as email, background jobs, file uploads and search are coming soon, so anything here that needs them is something you add yourself for now.

• Cross-encoder reranker and query rewriting: hybrid retrieval meets phase-1 quality targets; each extra call has a cost.
• Dedicated vector database or sharded pgvector: pgvector HNSW in the tenant database is sufficient and keeps data isolated.
• Tool use and agent workflows: side effects and loops are the main cost and safety risk.
• Connectors (Drive, Notion, web crawl) with incremental sync: needs OAuth, cursors and per-source quotas.

How detailed should a prompt for an AI document chatbot be?

Name the users, the main objects, one rule that must always hold and one thing to leave out. The plan fills in the rest, and you can change it before generation.

Which words tell Forge64 this is an AI document chatbot?

Phrases such as chatgpt, chat with your documents, chat with pdf and chat with your data point to this category.

Can I change the app after the first build?

Yes. Describe the change in plain English and Forge AI edits the app; the quality gate checks the code again.

• How to build an AI document chatbot
• AI document chatbot architecture
• AI document chatbot security checklist
• AI document chatbot scaling checklist
• Often combined: SaaS dashboard
• Often combined: CRM
• All app types