LunarCrush LLM | creator/twitter::41011818/posts

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[@nik_balaji](/creator/twitter/nik_balaji)
"2/n Synth Data Generation: If you *have* to create synthetic trace data do it in a smart skeptical way. * Wear a Product or Data Scientist hat * Think of all 'dimensions' of a user query. Specific eg. a) Features/Intent b) Persona c) Query Complexity"  
![@nik_balaji Avatar](https://lunarcrush.com/gi/w:16/cr:twitter::41011818.png) [@nik_balaji](/creator/x/nik_balaji) on [X](/post/tweet/1948808191732842726) 2025-07-25 18:10:54 UTC XXX followers, XX engagements


"Towards Physics-Guided Foundation Models(FMs): new approach of building domain-specific FMs for scientific/engineering tasks. Don't rely on big data/ gen-purpose architectures alone embed domain knowledge into the model arch objectives training data"  
![@nik_balaji Avatar](https://lunarcrush.com/gi/w:16/cr:twitter::41011818.png) [@nik_balaji](/creator/x/nik_balaji) on [X](/post/tweet/1949545853988835525) 2025-07-27 19:02:07 UTC XXX followers, XX engagements


"All about Error Analysis in LLM Evals some Notes 🧵: * Ideally you have traces that you can annotate for failure modes. Rule of thumb: XXX diverse traces * Reach 'theoretical saturation' * Don't just ask LLM's to give queries/traces"  
![@nik_balaji Avatar](https://lunarcrush.com/gi/w:16/cr:twitter::41011818.png) [@nik_balaji](/creator/x/nik_balaji) on [X](/post/tweet/1948808190466179343) 2025-07-25 18:10:54 UTC XXX followers, XX engagements


"6/n SideNote: Braintrust ai (and other enterprise grade LLM applications) provide: * Eval frameworks to test prompts and model outputs * Traces and logging for monitoring production usage * Playground environments for testing prompts and new models"  
![@nik_balaji Avatar](https://lunarcrush.com/gi/w:16/cr:twitter::41011818.png) [@nik_balaji](/creator/x/nik_balaji) on [X](/post/tweet/1948808197084729780) 2025-07-25 18:10:55 UTC XXX followers, XX engagements

[GUEST ACCESS MODE: Data is scrambled or limited to provide examples. Make requests using your API key to unlock full data. Check https://lunarcrush.ai/auth for authentication information.]

@nik_balaji "2/n Synth Data Generation: If you have to create synthetic trace data do it in a smart skeptical way. * Wear a Product or Data Scientist hat * Think of all 'dimensions' of a user query. Specific eg. a) Features/Intent b) Persona c) Query Complexity"
@nik_balaji on X 2025-07-25 18:10:54 UTC XXX followers, XX engagements

"Towards Physics-Guided Foundation Models(FMs): new approach of building domain-specific FMs for scientific/engineering tasks. Don't rely on big data/ gen-purpose architectures alone embed domain knowledge into the model arch objectives training data"
@nik_balaji on X 2025-07-27 19:02:07 UTC XXX followers, XX engagements

"All about Error Analysis in LLM Evals some Notes 🧵: * Ideally you have traces that you can annotate for failure modes. Rule of thumb: XXX diverse traces * Reach 'theoretical saturation' * Don't just ask LLM's to give queries/traces"
@nik_balaji on X 2025-07-25 18:10:54 UTC XXX followers, XX engagements

"6/n SideNote: Braintrust ai (and other enterprise grade LLM applications) provide: * Eval frameworks to test prompts and model outputs * Traces and logging for monitoring production usage * Playground environments for testing prompts and new models"
@nik_balaji on X 2025-07-25 18:10:55 UTC XXX followers, XX engagements