Product Docs
  • What is Dataworkz?
  • Getting Started
    • What You Will Need (Prerequisites)
    • Create with Default Settings: RAG Quickstart
    • Custom Settings: RAG Quickstart
    • Data Transformation Quickstart
    • Create an Agent: Quickstart
  • Concepts
    • RAG Applications
      • Overview
      • Ingestion
      • Embedding Models
      • Vectorization
      • Retrieve
    • AI Agents
      • Introduction
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    • Data Studio
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        • Single Dataflows:
        • Composite dataflows:
        • Benefits of Dataflows:
      • Discovery
        • How to: Discovery
      • Lineage
        • Features of Lineage:
        • Viewing a dataset's lineage:
      • Catalog
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  • Guides
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      • Configure LLM's
        • AWS Bedrock
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        • OpenAI Embedding Model
      • Connecting Your Data
        • Finding Your Data Storage: Collections
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        • Ingesting Unstructured Data
        • Unstructured File Ingestion
        • Html/Sharepoint Ingestion
      • Create Vector Embeddings
        • How to Build the Vector embeddings from Scratch
        • How do Modify Existing Chunking/Embedding Dataflows
      • Response History
      • Creating RAG Experiments with Dataworkz
      • Advanced RAG - Access Control for your data corpus
    • AI Agents
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      • Transformation Functions
        • Column Transformations
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            • Masking Operations
            • 1-way Hash Operation
            • Copy Operation
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            • Convert Operation
            • Vlookup Operation
          • Numeric Operations
            • Tiles Operation
            • Numeric Calculation Operations
            • Custom Calculation Operation
            • Numeric Encode Operation
            • Mask Operation
            • 1-way Hash Operation
            • Copy Operation
            • Convert Operation
            • VLookup Operation
          • Boolean Operations
            • Mask Operation
            • 1-way Hash Operation
            • Copy Operation
          • Date Operations
            • Date Format Operations
            • Date Calculation Operations
            • Mask Operation
            • 1-way Hash Operation
            • Copy Operation
            • Encode Operation
            • Convert Operation
          • Datetime/Timestamp Operations
            • Datetime Format Operations
            • Datetime Calculation Operations
            • Mask Operation
            • 1-way Hash Operation
            • Copy Operation
            • Encode Operation
            • Page 1
        • Dataset Transformations
          • Utility Functions
            • Area Under the Curve
            • Page Rank Utility Function
            • Transpose Utility Function
            • Semantic Search Template Utility Function
            • New Header Utility Function
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          • Window Functions
          • Case Statement
            • Editor Query
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          • Filter
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      • Data Prep
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          • Configuring a Join
        • Union
          • Configuring a Union
      • Working with CSV files
      • Job Monitoring
    • Utility Features
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      • Connect to data source(s)
        • Cloud Data Platforms
          • AWS S3
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        • Databases
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          • Couchbase
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        • SaaS Applications
          • Google Ads
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          • Zoom
          • JIRA
          • Salesforce
          • Zendesk
          • Hubspot
          • Outreach
          • Fullstory
          • Pendo
          • Box
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          • Slack
          • OneDrive / Sharepoint
          • ServiceNow
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      • Authentication
      • User Management
    • How To
      • Data Lake to Salesforce
      • Embed RAG into your App
  • API
    • Generate API Key in Dataworkz
    • RAG Apps API
    • Agents API
  • Open Source License Types
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  1. Guides
  2. AI Agents

Guidelines

For best and consistent results keep these guidelines in mind when creating Tools and Agents -

  • Use good names for tools and scenarios such that the name indicate their purpose. e.g. getOrderDetails is better then getO or doSomething. The Agent will use tool names to help improve its understanding of the tool during reasoning. So good names can help a lot.

  • Provide good descriptions for scenarios, tools, parameters and return fields

    • Descriptions help the Agent understand what exactly the tool does and anything it should know about the tool. You can provide information such as special cases, or instructions that the Agent should keep in mind. Typically, the Agent does not need to know the implementation mechanics of the tool - i.e.g whether it is Database query or a REST API call.

      • e.g. getOrderDetails returns an Order object for the specific orderId. orderId is required. If not provided, it must be requested.

    • Description of the scenario helps map the user intent to the scenario. If the correct scenario is not being picked up then it could be because there is overlap between different scenarios so more than one scenario maps to the same user intent or that the agent is unable to map the user intent appropriately. Updating the scenario description to include the missing intent description and/or additional examples will help.

  • Be consistent

    • Like us, Agents can get confused if terminology is inconsistent or different names are used to mean the same thing. e.g. if you are using model number in parts and then refer to it as part number elsewhere. The Agent might not be able to understand that they are the same.

  • Mention special requirements

    • Specific formats such for dates or ids should be mentioned so the Agent can follow them

    • Abbreviations or commonly used alternative terms can also be mentioned

    • Special cases can be specified by describing the situation in plain language

    • Enums can be specified by providing a list of possible values

  • Provide examples

    • If you have specific formats, or specific requirements, examples go a long way in helping the Agent understand what you might want. If an id has a specific format even though it is a string, you can provide the format and examples in the description.

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Last updated 1 month ago