Our cutting-edge conversational prototype leverages state-of-the-art techniques in natural language processing and generative AI. The system architecture is powered by a suite of large language models, including GPT-3. 5 and GPT-4, enabling natural language understanding and response generation capabilities.
The integration with Langchain provides an abstraction layer for seamless large-language model querying. Leveraging its capabilities, we built customized chains to enable robust transcription processing and analysis. This included chains for multi-step workflows like generating summaries, extracting action items, and identifying key points from transcripts.
The integration of question-answering functionality empowers users to extract salient information as needed. Additionally, we integrated sentiment analysis modules to gain insights into the contextual emotional tone and valence within the conversational transcripts. Prompt engineering techniques were employed to refine the chains for optimal performance on our specific dataset.
We assessed performance via two primary methods: user feedback gathered through interaction icons and automated evaluations. For each transcript, the system executes question generation and then answer prediction using multiple configurations. These predictions are compared to the generated answers by semantic similarity to identify optimal parameters.
In summary, the configurable pipeline and analysis toolkit enable data-driven optimization of cost and performance. Additionally, we conducted evaluations to assess the integration costs, benefits, and potential efficiency gains from incorporating third-party platforms like SageMaker and HuggingFace. This analysis explored pathways for optimizing the technology stack in future iterations.