To address this challenge, Bajaj Technology Services(BTS) developed a robust testing application has been developed utilizing AWS Bedrock's Anthropic Haiku Model, the AWS Knowledgebase, and Django. This system automates and scales the verification of chatbot accuracy, ensuring that responses meet the high standards required in regulated environments.
The need for accurate Chatbot responses
In regulated industries, the implications of inaccurate chatbot responses can be severe. For example, a miscommunication in financial advice could lead to significant repercussions for customers and the institution. Thus, it is essential to establish a rigorous testing framework that continuously assesses chatbot performance and accuracy. This framework ensures that as the AI model evolves, it remains aligned with both customer expectations and regulatory requirements.
Application Workflow
- Input & Expansion: The system begins by taking few user-provided questions and programmatically generating ‘x’variations for each. This results in a comprehensive pool of hundreds of questions, covering a wide array of potential user queries.
- Response Generation: These questions are processed by the LLM (AWS Anthropic Haiku Model), powered by AWS Bedrock, to produce answers.
- Cross-Checking for Accuracy: Each generated response is then compared against pre defined, human-curated answers stored in the AWS Knowledgebase. Accuracy is assessed by matching these responses with the established dataset, allowing for the identification of deviations or inaccuracies.
Key Features
- Scalability: Leveraging AWS Bedrock ensures the LLM can handle large batches of queries simultaneously, providing a robust infrastructure for testing.
- Automation: The automatic generation of additional questions eliminates the manual effort typically required, significantly accelerating the testing process.
- Accuracy Scoring: The application calculates accuracy scores for each batch of queries, delivering clear metrics on chatbot performance.
Technical Benefits
- Efficient Testing: Automating the generation and validation of responses streamlines the testing cycle, facilitating rapid feedback for ongoing improvements.
- Model Evaluation: By applying diverse question variations, the model's ability to generalize across contexts and user phrasing can be effectively assessed, ensuring adaptability to real-world use cases.
For organizations looking to optimize their chatbot’s performance and ensure the highest degree of accuracy, this application provides a scalable, automated solution. Contact BTStoday to learn how you can integrate this testing framework into your operations and elevate your chatbot’s effectiveness.
