Wikipedia Grounded Q&A AI Assistant

An AI assistant that answers user questions with factual and well-researched information, using the RIG approach to ground responses in Wikipedia sources and specify exact sections. Ideal for reliable, traceable answers based on external data.

How the AI Flow works - Wikipedia Grounded Q&A AI Assistant

How the AI Flow works

User Input Collection

Collects user questions through a chat interface.

Initial Draft Generation

Generates a draft answer and identifies which sections need external data or verification.

Wikipedia Data Retrieval

Uses the Wikipedia Tool to fetch relevant and factual information for each section of the answer.

AI Agent Fact-Checking & Refinement

AI Agent refines and grounds each section of the answer using the retrieved Wikipedia data, adding direct source links.

Response Delivery

Presents the grounded, well-sourced answer back to the user via the chat interface.

Prompts used in this flow

Below is a complete list of all prompts used in this flow to achieve its functionality. Prompts are the instructions given to the AI model to generate responses or perform actions. They guide the AI in understanding user intent and generating relevant outputs.

Prompt

Creates the initial LLM prompt to generate a sample answer with fake data and source indicators for further refinement. Guides the LLM to specify which sources ...

                Gived is user's query. Based on the User's query generate best possible answer with fake data or percentage. After each of different sections of your answer, include data which source to use in order to fetch the correct data and refine that section with correct data. you can either specify to choose Internal knowledge source to fetch data from in case there is custom data to user's product or service or use wikipedia to use as general knowledge source.

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Example Input: Which countries are top in terms of renewable energy and what is the best metric for measuring this and what is that measure for top country?
Example output: The top countries in renewable energy are Norway, Sweden, Portugal, USA [Search in Wikipedia with query "Top Countries in renewable Energy"], the usual metric for renewable energy is Capacity factor [Search in Wikipedia with query "metric for renewable energy"] and number one country has 20% capacity factor [search in Wikipedia "biggest capacity factor"]
---

Let's begin now!

User Input:   {input}

AI Agent

LLM agent prompt that instructs the model to refine an initial answer using Wikipedia tool, focus on factual accuracy, cite sources per section, and avoid gener...

                You are given a sample answer to user's question. The sample answer might include wrong data.&

use wikipedia tool in the given sections with the specified query to use wikipedia's information to refine the answer. 

include the link of wikipedia in each of the sections specified. 

FETCH DATA FROM YOUR TOOLS AND REFINE THE ANSWER IN THAT SECTION. ADD THE LINK TO THE SOURCE IN THAT PARTICULAR SECTION AND NOT IN THE END.


Focus on detailed information. Don't use phrases like "In todays fast changing world...", "In today's complex...", "is a crucial step", "plays significant role", "fast-paced...", "pivotal role", "In the ever-evolving landscape of" or "In the realm of ...", always cut to the point without useless conclusions or intros.

Components used in this flow

Below is a complete list of all components used in this flow to achieve its functionality. Components are the building blocks of every AI Flow. They allow you to create complex interactions and automate tasks by connecting various functionalities. Each component serves a specific purpose, such as handling user input, processing data, or integrating with external services.

Flow description

Purpose and benefits

Overview

The RIG (Retrieval Interleaved Generator) Wikipedia Assistant is an automated workflow designed to answer user queries by generating initial responses, identifying necessary factual data, retrieving information from Wikipedia, and refining its answers with precise citations for each section. Its primary goal is to provide answers that are grounded in verifiable sources and to specify exactly which sections and sources were used, making it especially useful for research, fact-checking, and educational purposes.

How the Workflow Operates

  1. Chat Initiation & Welcome

    • When a chat session is opened, the user is greeted with a welcome message explaining the flow’s purpose: providing reliable, source-backed answers. This helps set expectations for the quality and transparency of responses.

  2. User Query Intake

    • The user submits a question through the chat input. This input is captured and passed along for processing.

  3. Prompt Generation

    • The workflow includes a Prompt Template that takes the user’s question and constructs a detailed prompt. This prompt instructs the system to:
      • Generate a draft answer, even if it uses placeholder data.
      • For each section in the answer, specify which external source (like Wikipedia) or internal knowledge base should be used to verify and refine that section.
      • Include search queries for Wikipedia to fetch the correct information for each section.

    Example:

    User Input: Which countries are top in terms of renewable energy?
Draft Output: The top countries are Norway, Sweden, Portugal [Search in Wikipedia: "Top Countries in renewable Energy"]...

  4. Initial Answer Generation

    • Using a language model generator, the system creates an answer draft based on the prompt, highlighting where factual data needs to be inserted and which sources to use for verification.

  5. Data Retrieval & Answer Refinement

    • An AI Agent receives the draft answer and leverages the Wikipedia Tool to search Wikipedia for the specified queries.
    • For each section of the answer, the agent retrieves the relevant factual data from Wikipedia and replaces the draft or placeholder content.
    • Each section is refined to include a direct link to the exact Wikipedia article or section used, ensuring transparency and easy verification.

    The agent is instructed to avoid generic or filler phrases, focusing only on concise, factual content.

  6. Final Output

    • The fully refined answer, with each section grounded in a specific Wikipedia source (and links provided inline), is displayed to the user in the chat interface.

Workflow Structure

StepComponentPurpose
1Chat Opened TriggerDetects new chat session and triggers welcome message
2Message WidgetDisplays initial greeting and instructions
3Chat InputAccepts user’s question
4Prompt TemplateFormats prompt with instructions for draft answer + source pointers
5GeneratorProduces initial answer draft (with placeholders)
6Wikipedia ToolEnables data retrieval from Wikipedia
7AI AgentRefines draft, fetches facts, inserts citations/links
8Chat OutputPresents the final, grounded answer to the user

Key Features and Benefits

  • Source Transparency: Each section of the answer clearly specifies which Wikipedia page or section was used, including direct links for user verification.
  • Automation & Scale: The workflow automates the process of drafting, fact-checking, and refining answers, making it suitable for handling many queries efficiently.
  • Research-Grade Output: By grounding every claim in a verifiable external source, the system produces answers suitable for academic, business, and professional contexts.
  • Customizability: If needed, internal knowledge sources can be plugged in alongside Wikipedia, making the system adaptable for company-specific data retrieval.

Use Cases

  • Educational Assistants: Provide students with answers that always cite their sources.
  • Fact-Checking Bots: Instantly verify information and present sources without manual research.
  • Customer Support: Deliver company or product information with clear data provenance.
  • Content Creation: Writers and journalists can get draft content with embedded references for further development.

Summary

This workflow empowers users with trustworthy, well-referenced answers by interleaving generation and retrieval steps. It is especially useful wherever factual accuracy, transparency, and source attribution are crucial. Its modular, automated design makes it highly scalable for organizations seeking to automate research and Q&A tasks at scale.

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