Expertise
Project Management
Frontend Development
Backend Development
Industry
IT
Timeline
6 weeks
Tech stack
ChatGPT API, LangChain
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.
In developing our Generative AI Prototype, we aimed to provide a platform where users could browse meeting notes created from transcriptions, ask questions about recorded meetings, and receive detailed answers without needing to sift through the notes.
However, this ambitious vision posed several unique challenges:
With our tool, you can effortlessly garner insights from meetings without the hassle of reviewing extensive notes or long recordings.
It utilizes pioneering natural language processing techniques to transcribe, analyze, summarize, and highlight the core points of meetings. Not just that, it enables users to interact with a chatbot for real-time access to summarized data, streamlining the process of fetching specific details or clarifications.
Say goodbye to laborious manual searches as our Generative AI Prototype brings all necessary information to your fingertips with a simple chatbot query.
Expertise
Project Management
Frontend Development
Backend Development
Industry
IT
Timeline
6 weeks
In developing our Generative AI Prototype, we aimed to provide a platform where users could browse meeting notes created from transcriptions, ask questions about recorded meetings, and receive detailed answers without needing to sift through the notes.
However, this ambitious vision posed several unique challenges:
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.
With our tool, you can effortlessly garner insights from meetings without the hassle of reviewing extensive notes or long recordings.
It utilizes pioneering natural language processing techniques to transcribe, analyze, summarize, and highlight the core points of meetings. Not just that, it enables users to interact with a chatbot for real-time access to summarized data, streamlining the process of fetching specific details or clarifications.
Say goodbye to laborious manual searches as our Generative AI Prototype brings all necessary information to your fingertips with a simple chatbot query.
