TutorialsOfficial documentation7 min readTutorials

Build FastGPT Core Application Use Cases

This page delivers a structured walkthrough of four core FastGPT application types, enabling users to quickly understand platform capabilities.

Overview

This page delivers a structured walkthrough of four core FastGPT application types, enabling users to quickly understand platform capabilities. It is intended for first-time FastGPT users, as well as pre-sales, delivery, operations, legal, and administrative roles seeking to evaluate or run the platform. Over the walkthrough, users will build four applications in sequential order: a corporate email writing conversational agent, a Civil Code Q&A assistant combining a dataset and conversational agent, a content review and automatic rewriting workflow, and an intelligent data analysis Agent V2. When working through each application, users should focus on three core objectives: identifying the problem each app type solves, understanding the reasoning behind key configuration choices, and observing validation outcomes post-deployment. All provided parameters and prompts serve as reusable starting points; for production deployments, users may substitute their own business materials, review rules, notification channels, and data files.

Required Preparations

Before beginning the walkthrough, gather the following mandatory assets:

  • An available AI model, such as GLM-5.1 or other pre-configured models
  • A dataset test file, for example the Civil Code, company policies, or product manuals
  • An email SMTP authorization code, to test the email tool functionality
  • A sample Excel or CSV file, to test the Agent V2 intelligent data analysis feature

Sequential Application Builds

Each application targets a distinct use case, with core configurations aligned to its intended function:

  1. Conversational Agent (Corporate Email Writing Assistant): Designed to draft professional corporate correspondence. Key configurations will standardize tone and structure for business emails, with validation focused on generating accurate, context-appropriate draft content.
  2. Dataset + Conversational Agent (Civil Code Q&A Assistant): Built to answer queries using a curated internal dataset. Key configurations will link the target dataset to the conversational agent framework, with validation focused on retrieving relevant dataset content to respond to legal questions.
  3. Workflow (Content Review and Automatic Rewriting): Orchestrates automated content checks and revisions. Key configurations will define formal review rules and rewriting logic, with validation focused on consistent application of review standards and accurate content modification.
  4. Agent V2 (Intelligent Data Analysis Agent): Processes and analyzes structured data files. Key configurations will connect the agent to the sample Excel or CSV file, with validation focused on generating meaningful, actionable data insights.

Source: FastGPT official source

Applicability and version scope

Use this page for the documented Tutorials scenario. Confirm the FastGPT, dependency, API, and deployment versions in the official source before applying a change.

Safety guardrails

Use [REDACTED_CREDENTIAL] for credentials and private data. Confirm the documented environment and version before review.

Rollback guidance

Restore the prior technical-content authority snapshot. Restore saved configuration and data snapshots, then repeat the smallest verification scenario.