Many organizations around the world are adopting GenAI technologies in their workflow to make their teams more productive and to achieve business outcomes that drive business growth.

Technical writers have a huge role in the GenAI era in ensuring trust in GenAI system-generated responses. Technical writers can produce GenAI-friendly content, help train the GenAI systems to produce the right responses based on human feedback, and also evaluate the responses of the GenAI system before deploying it in the production environment.

Things to Consider in Evaluating GenAI Responses

1. Relevancy

The GenAI-generated response should be relevant to the customers’ questions/prompts. The generated response will be relevant if the underlying retrieved mechanism retrieves relevant chunks from the knowledge base. Thus, it is important to look at evaluation metrics about relevancy

2. Accuracy

Trust is fundamental in ensuring the adoption of GenAI-based agents. Accuracy plays a crucial role in evaluating the GenAI response. Accuracy metrics can be computed by comparing the GenAI response with the ground truth

3. User Feedback

User feedback plays another important role in trust. If GenAI responses are not relevant or non-factual, users can flag them for inaccuracy. This should be considered to retrain the GenAI-based agent to produce accurate responses over time

4. Error Handling

If GenAI responses cannot be generated, the response should be courteous

5. Response Time

User experience is affected by response time. If the response time is longer, then the user has to wait and they might abandon using GenAI-based agents. A typical balance has to be attained between user experience, cost, and accuracy.

Framework to Evaluate GenAI Responses

Technical writers are best suited to evaluate the responses generated by GenAI-based assistive search as they curate accurate information across the organization and interact with many subject matter experts. The responses from GenAI-based assistive search are very subjective; thus, it is important to create some baseline around GenAI-based assistive search responses through some numerical metrics.

These metrics can guide improvising responses by either tweaking the underlying content or tweaking the GenAI-based assistive search tool’s functional parameters, such as system messages, chunk size, etc.

Two open-source frameworks are available to evaluate the responses generated by GenAI-based assistive search.

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