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Overview

The quality of your agent’s answers comes down to four things you control: the instructions you give it, the data sources you train it on, the model you pick, and how you correct it once it’s live. This page covers each in turn.
Work through these in order. Instructions and data sources account for most quality problems, so fix those before reaching for a different model.

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Refine the AI agent’s instructions

The instructions shape your agent’s behavior and responses. Use them to set a persona, define tone, or specify the types of questions the agent can answer. Clear, precise instructions keep the agent aligned with your goals and your user experience. This is the default, which you can edit or replace:
To keep the agent from answering outside your training data, say so explicitly. For example: “You will only provide answers based on the information from your training data.” Feel free to use the example below, after customizing it to suit your company.
For more detail on refining your instructions, see Instructions.

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Improve readability of data sources

The quality of your agent’s responses depends heavily on the quality of the data sources you provide. Chatbase relies on readable text to generate accurate responses, so make sure the websites or PDFs you upload contain readable text. Some websites aren’t scraper-friendly. If your agent struggles to answer questions based on your website, this could be why. Work around it by copying the information in as text, or uploading it as a PDF.
Chatbase can’t process images, videos, or other non-textual elements in documents.
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Add suggestable images

To display images in the chat bubble, have the agent send image links in markdown format. The URL must end in .png or .jpg for the image to render. You can also use a widget. You can include a line like this in your instructions to display an image after each response:
Once added, every agent response displays the image:
An agent response with an image rendered beneath it

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Choose the right AI model

Selecting the right model matters for performance. Match it to your use case, considering task complexity and speed: a model suited for structured data is ideal for data-heavy tasks, while a conversational model works best for customer support. Also consider scalability. Choose a model that can grow with your needs, handling more data while maintaining accuracy. Some models are better for real-time interactions, others excel in batch processing.

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Compare models side by side

To see how each model responds, go to Build > Instructions and click Compare in the header. You can run the same message against several models at once and judge:
  • Response quality and accuracy
  • Response time and speed
  • Tone and personality
  • Handling of edge cases
If you’re unsure which model to use, see the models comparison page.

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Use Revise and Q&A to correct answers

Two features let you fix a wrong answer so it stays fixed. Add Q&A’s gives the agent exact answers for specific questions. On Build > Data sources, click Add Q&A’s. When a user asks something similar to one of your saved questions, the agent replies with your predefined answer word-for-word. Revise corrects an answer after the fact. Monitor your agent’s responses under Activity > Conversations. When you find an answer you’d like to change, click the revise button and adjust it. The revised answer is saved as a Q&A data type, so the agent references it for future queries. Revised responses appear on Build > Data sources with a Q/A badge.
Revising an agent answer from the conversation log

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Next steps

Instructions

The full reference on instructions, guardrails, model, and temperature.

Data sources

Add, organize, and retrain on the content your agent learns from.

Models comparison

See how the available models differ before you commit to one.

Analytics

Track conversations and measure whether your changes actually helped.