- point of diversity - use cases (tools, bot prompt types, kbs) | user persona (user characteristics, conversation charactersitics)
- conversation characteristics - recalls, length, personalisation, errors




DATA GENERATION:
1. econmomic times india - 2022,23,24,25
2. https://www.bls.gov/



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memory protocols - different sort of memories
how to handle memory 
decide what to forget
long horizon context - 10hrs
human in loop - pause and resume - what all is done
sql on large number of rows deep queries
good hypothesis of what to test - like dfs is a better way to solve the problem
deep research report - mckinsey reports - language and ways
generate long documents
4. verification and self check loops - first i need to have confidence and then increase the confidence - what is important to verify here


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- tool output conflicts with actual variables





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## Moving To RL
- adding verifier - add small verifier after we get the trajectory



## Overall
- Looking at Arya for data gen
- Looking at Sierra and other workflow providers for data gen

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// quantity works better than quality in data gen with llm --> generate more number of samples and then dedup rather than constraining on a smaller quality set
    lesser - 5 companies 30 use case --> 11/30 kept
    more - 5 companies 60 use cases --> 15/60 kept

removing stakeholders with X cross mapping


## Types of errors:
1. user prompt was coming in hindi because of user language = fixed by prompting
2. user did not comply with tool results - tool said order had tomoato, banana - user said issue in tomato and spinach



--> gave complexity rubric for state budget