What you’ll learn
  • How to format your documents properly?
  • Examples: How to Format Your Documents
  • How to update your prompt for best knowledge-base search?

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How RAG (Retrieval Augmented Generation) Works?

Retrieval Augmented Generation (RAG) enhances large language models (LLMs) by supplementing them with external knowledge. When a query is asked, the system retrieves relevant chunks of information from the knowledge base and feeds them into the model alongside the query. This ensures responses are more accurate, grounded, and contextually relevant.

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How to Format Your Documents?

  • Use clear structure: Organize content with headings, subheadings, and bullet points to improve navigation.
  • Keep it simple: Write in plain, jargon-free language so users with minimal technical knowledge can understand.
  • Optimize for retrieval: Ensure important information is stated explicitly in text. Avoid burying key details in images or tables without descriptive text.
  • Supported formats: Most common formats like DOCX, Markdown, Excel, and PDF are supported. Arrange and format text according to your file system for consistency.

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Examples: How to Format Your Documents

Here are some concrete examples to help you apply the best practices:

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Example 1: Clear Structure

Bad: 
System requirements: Windows 10, 8 GB RAM, Installation steps: Download the file, Run installer, Follow prompts, Done.
Good: 
### System Requirements
- Windows 10
- 8 GB RAM

### Installation Steps
1. Download the file
2. Run the installer
3. Follow the prompts
4. Installation complete

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Example 2: Simple, Jargon-Free Language

Bad: 
The application leverages a multi-threaded paradigm for expedited data processing.
Good: 
The application uses multiple threads to process data faster.

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Example 3: Optimize for Retrieval

Bad: 
![Installation steps screenshot](install.png)
Good: 
Installation Steps:
1. Download the file from our official site.
2. Double-click the installer.
3. Accept the license agreement.
4. Choose the installation folder.

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Important Note on Table Retrieval

While text-based tables (Markdown, CSV, Excel) are far better than images for retrieval, they still require careful design for best performance:
  • Keep headers clear and descriptive: Avoid abbreviations that may confuse the search. For example, use Storage (GB) instead of just Stor.
  • Use consistent units: Always specify units in the header or the value (e.g., 10 GB vs just 10).
  • Avoid merging cells: Complex merged cells in spreadsheets make retrieval harder. Keep one value per cell.
  • Add context in surrounding text: Before or after the table, explain what the table contains. For example: “The following table shows pricing plans and their features.”
Better Example: 
The following table shows pricing plans and their features:

| Plan   | Price | Storage (GB) | Support        |
|--------|-------|--------------|----------------|
| Plan A | $10   | 10           | Email support  |
| Plan B | $20   | 50           | Phone support  |
| Plan C | $50   | 200          | 24/7 support   |
This way, both the table structure and the explanatory text are retrievable, making searches more reliable.

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Example 4: Supported Formats

Bad: 
All information stored in a single large image file.
Good: 
Use DOCX, PDF, or Markdown with headings and text descriptions so search tools can retrieve information easily.

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How to Update Your Prompts for Best Knowledge-Base Search?

  • Clarify tool usage: In your system prompt, explicitly state under which conditions the knowledge-base search tool should be triggered.
  • Language consistency: Always specify that the query language must match the document language. For example, if documents are in English, include: “The knowledge-base-search tool query must be in English.”
  • Be explicit: Provide clear instructions for how the retrieval tool should parse and interpret queries.
  • Context matters: Tailor prompts to explain how retrieved knowledge should be used in the response (e.g., grounding answers, summarizing docs, or citing evidence).