Guides

Mastering your Knowledge Graph : Structuring Smarter Data

A well-structured Knowledge Graph transforms your AI agent from a keyword-matcher into a strategic assistant that understands context, connects insights, and delivers accurate responses. Here’s how to get the most value from it.

LLM vs Agent: What’s the Difference?

LLM (Large Language Model) = A smart friend that answers based on past training.
Agent = A smart assistant that can look things up, use tools, recall prior context, and take action.

The Knowledge Problem

Let’s say you’re building a customer service agent for a pizza restaurant.

You have an Excel file with:

  • Menu items and prices
  • Delivery zones
  • Order history
  • Customer preferences

❌ The Wrong Way: File Upload + Search

Uploading a raw spreadsheet leads to:

  • Keyword-based retrieval with no context
  • No concept of relationships or priority
  • Poor handling of nuanced queries

Example failure: “What’s popular in downtown?” → Returns unrelated results just because the keyword appears.

✅ The Right Way: Structured Knowledge Graph

Build a web of connected insights, not a stack of disconnected facts.

Step 1: Break Down Your Data

Bad:

Row 1: Margherita, $12, Downtown, 4.5 stars, 150 orders

Good:

MENU ITEM: "Margherita Pizza costs $12. This is a popular item in the Downtown delivery zone with a 4.5-star rating and 150 total orders. Customers often order this with garlic bread."
LOCATION DATA: "Downtown delivery zone has high demand for vegetarian options. Margherita and Veggie Supreme are the top sellers here."
CUSTOMER INSIGHT: "Customers who order Margherita pizza have a 70% repeat order rate."

Step 2: Add Context to Each Entry

Ask:

  • What is this? (Menu item? Trend? Rule?)
  • Why does it matter?
  • How should the agent use it?

Step 3: Connect the Concepts

A good knowledge graph:

  • Links Margherita Pizza to vegetarian options
  • Links Downtown to delivery zones and popular dishes
  • Links 4.5 stars to recommendations and satisfaction

Real Example: File Search vs Graph Response

Customer asks: “I’m in downtown and want something healthy.”

File Search Result: Finds unrelated rows containing “downtown” and “healthy”.

Knowledge Graph Result:

  1. Recognizes “downtown” = delivery zone
  2. Interprets “healthy” as vegetarian/low-calorie
  3. Connects to Margherita Pizza
  4. Responds with helpful, targeted suggestion

Key Principles for Knowledge Graph Structuring

  1. One concept per entry
  2. Rich context — go beyond just facts
  3. Clear relationships between concepts
  4. Action-oriented — guide the agent on usage

Quick Checklist

Before adding knowledge:

  • Is this self-contained and meaningful on its own?
  • Did I explain its purpose?
  • Did I define how/when it should be used?
  • Are related topics clearly connected?
  • Would someone unfamiliar with the raw file understand it?

Troubleshooting Tips

  • Agent returns irrelevant results? Break entries into simpler, more focused pieces.
  • Context missing? Add usage guidance for each fact (e.g., “use this for vegetarian recs”).
  • Repeating info? Merge overlapping insights to reduce noise.
  • Too much detail? Trim extra wording and keep entries concise but informative.

Benefits of Structured Knowledge Graphs

  • Smarter, more accurate agent responses
  • Better handling of real-world, layered user questions
  • Easier knowledge updates without retraining
  • Actionable insights instead of shallow keyword matches
  • Improved trust, performance, and user satisfaction