Some questions and answers take days or weeks, and sometimes the approach moves from LLM to NLP, It's a blend of techniques to make things work.
- How do we optimize RAG with internal documents, Original vs Summary vs Intents, What works best?
- How do we merge external data? How can we keep versions and relevance?
- More than LLM work, The heavy lifting is for Data preprocessing/cleaning / Embedding on summary
- When to use LLM vs Multimodals?
- What is the benchmark for our domain and how much do we meet it consistently?
- The transition for LLM, LLM+KG, Creating the data mapping..
A lot of challenges but one at a time, Balancing Consistency, Accuracy, and Latency. If you want to solve real problems you can connect/explore potential learning experimentation opportunities / dedicate some learning hours. Please drop a note to career@proplens.ai
#learnings #NLP #Datascience #RAG #LLMs #perspectives #Datascience
Keep Exploring!!!
