How to implement Chatbots in an industrial Context | Christoph Windheuser

Testing

Discover how to implement chatbots in an industrial context with a human-centered approach, leveraging open-source frameworks and test-driven development to ensure successful implementation.

Key takeaways

Chatbots should be designed for human-like conversations, not technical wizardry.
Intent recognition and design are crucial for successful chatbot usage.
Use open-source frameworks and technology stacks, such as TensorFlow, Keras, and Rasa.
Avoid overfitting and handling unknown intents and utterances are challenges in chatbot development.
Ensure testability and continuous testing using approaches like test-driven development (TDD) and integration testing.
Focus on the intent, not just processing speech or text.
Test comprehensibility, ability to convey information, and usability of a chatbot.
Design chatbots’ communication to be clear, unambiguous, helpful, and concise.
Training models and dealing with out-of-dictionary words are challenges in voice chatbots.
Different environments may require different chatbot designs or frameworks.
Use word embeddings for intent recognition, such as Word2Vec.
Use APIs or voice recognition engines to connect bot and external systems.
Some restrictions and limitations in current technological options.
Continuous exploration, expansion, and improvement of chatbots.

How to implement Chatbots in an industrial Context | Christoph Windheuser

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