The blog has been put together by Gaurav Lagad from The Apprentice Project (firstname.lastname@example.org)
About the org and use-case
- Automated Student and Teacher Support – P1
- Queries related to program and content(activities)
- Support for Multiple languages
- Voice note based queries’ support
- LLM for Churn Analysis – P2
- User Feedback Analysis
- User Profiling & Customised learning pathways
- Early Prediction system
- LLM for Connecting Opportunities to Students – P3
- User Sentiment Analysis & Segmentation done for by LLM (to understand the users’ rigors)
- Opportunities catalog ingested by TAP
- Please 3-5 points that you’re taking away from the conversations/discussions that helped your understanding of the LLM technology or helped you get ideas on how it might be applicable / not applicable for your use case.
- LLM for doing Data Analysis – Edmund’s talk about the usage of ChatGPT 4 to use for data analysis makes me want to explore the same for TAP’s use-cases where we require data analysis to be done.
- LLM for LLM – Usage of LLM for doing meta-tasks like classification (multi-shot classification), and language detection. This will help us reduce the cost of the subsequent calls and make our LLM implementation more robust.
- Art of Prompt Designing – The difference between Prompt Designing and Knowledge base and its role in the way the ChatGPT responds. The significance of good grammar, explicit communication while designing a prompt and its repercussions on the way ChatGPT responds.
- OpenAI Playground – We can use the OpenAI playground in order to effectively experiment with the ChatGPT prompts, knowledge base and test out our LLM implementation before testing out the whole ChatGPT implementation E2E.
- Edmund demonstrated writing code in python to do the LLM for LLM bit wherein Edmund helped me explore the idea of talking to LLM for intent classification/evaluation of the response.
- Aman demonstrated usage of OpenAI playground to test the LLM implementations quickly.
- This screenshot demonstrates the usage of playground to test out the variable injection part
Potential Next steps / Help needed
- Experimentation using LLM from the learnings in the Glific sprint. No help needed right now as such, will come back whenever there is any help needed 🙂
Overall thoughts on the sprint
- Great execution of the entire sprint! It was a great idea to bring everyone together. Loved the way Tejas coordinated (great time keeper 🙂 ) .
- Something I loved that Tejas mentioned – “We are limited by our own imagination”. This I loved really well since I personally do agree with the same and this sprint was an eye opener for me where I didn’t know many of the use-cases of LLM.
- One realization I had was how everyone’s problems are overlapping and how this sprint was such a good place to come, discuss, share and learn 🙂
- Nothing in terms of AODs as of now! 🙂