Ongoing testing: experiments using Contextual Answers RAG TSM + Jamba Instruct model
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create public facing content, blog, webinar, etc.
Prioritize 1-2 UI elements to show data based on criteria
This will depend on chosen evaluations in pipeline
slide building
update and finish PoC deck for meeting with Z
Presentation + deep dive discussion will involve these topics: the business problem, business value of concept, fitness of the solution, implementation details and demo of the results, conclusions, limitations, next steps
No need for code details -- focusing on functionality of solution using AI21 AI technology
Deck link - WIP
Revise evaluation pipeline to output more formatted CSV + use labeled speakers transcript
Decide on MVP-level ranking of 1-2 traits based on transcript. Criteria for prod dependent on HR communication
Verify ability to use Jamba Instruct or Jamba for implementation especially chat
Find out how to use with Python SDK or if not able, what to do otherwise
Code Streamlit UI setup to utilize app data
Create slide deck and prep content for RAGCamp session on AI21 PoC
prompt engineering to improve results
improve evaluation results and citation with Jamba model by changing the prompt template
Build query pipeline passing uploaded documents into prompt context
Use Python SDK to include document matches in prompt context when the "evaluate" function is triggered to use in comparison with role-specific rubric to determine if candidate fulfills rubric line item or not.
When "evaluate" is triggered (in the future, by user via UI button after uploading their documents) the appropriate rubric criteria for the role acts as a query used by Jamba LLM against context documents
For PoC, context documents are: candidate resume, interview transcript
Write and test Python script to ingest documents to RAG engine vector store
Revise RAG pipeline to calculate & parse criteria requested by TW HR
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