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Alignment is Real // Shiva Bhattacharjee // #260

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Shiva Bhattacharjee is the Co-founder and CTO of TrueLaw, where we are building bespoke models for law firms for a wide variety of tasks. Alignment is Real // MLOps Podcast #260 with Shiva Bhattacharjee, CTO of TrueLaw Inc. // Abstract If the off-the-shelf model can understand and solve a domain-specific task well enough, either your task isn't that nuanced or you have achieved AGI. We discuss when is fine-tuning necessary over prompting and how we have created a loop of sampling - collecting feedback - fine-tuning to create models that seem to perform exceedingly well in domain-specific tasks. // Bio 20 years of experience in distributed and data-intensive systems spanning work at Apple, Arista Networks, Databricks, and Confluent. Currently CTO at TrueLaw where we provide a framework to fold in user feedback, such as lawyer critiques of a given task, and fold them into proprietary LLM models through fine-tuning mechanics, resulting in 7-10x improvements over the base model. // MLOps Jobs board https://mlops.pallet.xyz/jobs // MLOps Swag/Merch https://mlops-community.myshopify.com/ // Related Links Website: www.truelaw.ai --------------- ✌️Connect With Us ✌️ ------------- Join our slack community: https://go.mlops.community/slack Follow us on Twitter: @mlopscommunity Sign up for the next meetup: https://go.mlops.community/register Catch all episodes, blogs, newsletters, and more: https://mlops.community/ Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Connect with Shiva on LinkedIn: https://www.linkedin.com/in/shivabhattacharjee/ Timestamps: [00:00] Shiva's preferred coffee [00:58] Takeaways [01:17] DSPy Implementation [04:57] Evaluating DSPy risks [08:13] Community-driven DSPy tool [12:19] RAG implementation strategies [17:02] Cost-effective embedding fine-tuning [18:51] AI infrastructure decision-making [24:13] Prompt data flow evolution [26:32] Buy vs build decision [30:45] Tech stack insights [38:20] Wrap up

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440 episodes

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Manage episode 439749626 series 3241972
Content provided by Demetrios. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Demetrios or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://ppacc.player.fm/legal.

Shiva Bhattacharjee is the Co-founder and CTO of TrueLaw, where we are building bespoke models for law firms for a wide variety of tasks. Alignment is Real // MLOps Podcast #260 with Shiva Bhattacharjee, CTO of TrueLaw Inc. // Abstract If the off-the-shelf model can understand and solve a domain-specific task well enough, either your task isn't that nuanced or you have achieved AGI. We discuss when is fine-tuning necessary over prompting and how we have created a loop of sampling - collecting feedback - fine-tuning to create models that seem to perform exceedingly well in domain-specific tasks. // Bio 20 years of experience in distributed and data-intensive systems spanning work at Apple, Arista Networks, Databricks, and Confluent. Currently CTO at TrueLaw where we provide a framework to fold in user feedback, such as lawyer critiques of a given task, and fold them into proprietary LLM models through fine-tuning mechanics, resulting in 7-10x improvements over the base model. // MLOps Jobs board https://mlops.pallet.xyz/jobs // MLOps Swag/Merch https://mlops-community.myshopify.com/ // Related Links Website: www.truelaw.ai --------------- ✌️Connect With Us ✌️ ------------- Join our slack community: https://go.mlops.community/slack Follow us on Twitter: @mlopscommunity Sign up for the next meetup: https://go.mlops.community/register Catch all episodes, blogs, newsletters, and more: https://mlops.community/ Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Connect with Shiva on LinkedIn: https://www.linkedin.com/in/shivabhattacharjee/ Timestamps: [00:00] Shiva's preferred coffee [00:58] Takeaways [01:17] DSPy Implementation [04:57] Evaluating DSPy risks [08:13] Community-driven DSPy tool [12:19] RAG implementation strategies [17:02] Cost-effective embedding fine-tuning [18:51] AI infrastructure decision-making [24:13] Prompt data flow evolution [26:32] Buy vs build decision [30:45] Tech stack insights [38:20] Wrap up

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440 episodes

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