Language AI Playbook
  • 1. Introduction
    • 1.1 How to use the partner playbook
    • 1.2 Chapter overviews
    • 1.3 Acknowledgements
  • 2. Overview of Language Technology
    • 2.1 Definition and uses of language technology
    • 2.2 How language technology helps with communication
    • 2.3 Areas where language technology can be used
    • 2.4 Key terminology and concepts
  • 3. Partner Opportunities
    • 3.1 Enabling Organizations with Language Technology
    • 3.2 Bridging the Technical Gap
    • 3.3 Dealing with language technology providers
  • 4. Identifying Impactful Use Cases
    • 4.1 Setting criteria to help choose the use case
    • 4.2 Conducting A Needs Assessment
    • 4.3 Evaluating What Can Be Done and What Works
  • 5 Communication and working together
    • 5.1 Communicating with Communities
    • 5.2 Communicating and working well with partners
  • 6. Language Technology Implementation
    • 6.1 Navigating the Language Technology Landscape
    • 6.2 Creating a Language-Specific Peculiarities (LSP) Document
    • 6.3 Open source data and models
    • 6.4 Assessing data and model maturity
      • 6.4.1 Assessing NLP Data Maturity
      • 6.4.2 Assessing NLP Model Maturity:
    • 6.5 Key Metrics for Evaluating Language Solutions
  • 7 Development and Deployment Guidelines
    • 7.1 Serving models through an API
    • 7.2 Machine translation
      • 7.2.1 Building your own MT models
      • 7.2.2 Deploying your own scalable Machine Translation API
      • 7.2.3 Evaluation and continuous improvement of machine translation
    • 7.3 Chatbots
      • 7.3.1 Overview of chatbot technologies and RASA framework
      • 7.3.2 Building data for a climate change resilience chatbot
      • 7.3.3 How to obtain multilinguality
      • 7.3.4 Components of a chatbot in deployment
      • 7.3.5 Deploying a RASA chatbot
      • 7.3.6 Channel integrations
        • 7.3.6.1 Facebook Messenger
        • 7.3.6.2 WhatsApp
        • 7.3.6.3 Telegram
      • 7.3.7 How to create effective NLU training data
      • 7.3.8 Evaluation and continuous improvement of chatbots
  • 8 Sources and further bibliography
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  1. 3. Partner Opportunities

3.2 Bridging the Technical Gap

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Last updated 1 year ago

Some organizations have a technical team who have already developed and launched digital tools. Other organizations are just starting and have very little or no technical capacity. Whatever your situation, there are some principles and best practices that your organization should follow. This will help you to build solutions that have a positive impact.

If a tech solution is to be useful and valuable, you’ll need to consider the following:

  • To start the process, you need to define the problem and understand the target audience’s needs. You also need to find out if they have access to technology. And you need to know what languages they speak.

  • It’s important to build a solid use case. To develop a use case, the organization needs to know who they want to reach. They need to know where the communication gaps are, and what impact they are hoping for.

  • Technical solutions should start with the problem and aim to solve it, not the other way around.

  • The solution should become part of programs that are already running and should deal with current communication problems.

  • A well-defined use case and problem statement will make the project more successful. It will also have a bigger impact than if the solution is driven by tech.

  • It’s important to involve users, the target audience, and those working on the design process. They should be involved at all stages. This will make sure that the solution is user-centered. It should then also reflect the needs and hopes of the target audience and the aims of the organization.

To learn more about how to develop a strong use case and identify program requirements, please see Section

4. Finding use cases that will have an impact