Vikram Rangnekar is an open-source software developer focused on simplifying LLM integration. He created LLMClient, a TypeScript library inspired by Stanford's DSP paper. With years of experience building complex LLM workflows, he previously worked as a senior software engineer at LinkedIn on Ad Serving.
Ax a New Way to Build Complex Workflows with LLMs // MLOps Podcast #259 with Vikram Rangnekar, Software Engineer at Stealth.
// Abstract
Ax is a new way to build complex workflows with LLMs. It's a typescript library based on research done in the Stanford DSP paper. Concepts such as prompt signatures, prompt tuning, and composable prompts help you build RAG and agent-powered ideas that have till now been hard to build and maintain. Ax is designed for production usage.
// Bio
Vikram builds open-source software. Currently working on making it easy to build with LLMs. Created Ax a typescript library that abstracts over all the complexity of LLMs, it is based on the research done in the Stanford DSP paper. Worked extensively with LLMs over the last few years to build complex workflows. Previously worked as a senior software engineer with LinkedIn on Ad Serving.
// MLOps Jobs board
https://mlops.pallet.xyz/jobs
// MLOps Swag/Merch
https://mlops-community.myshopify.com/
// Related Links
The unofficial DSPy framework. Build LLM-powered Agents and "Agentic workflows" based on the Stanford DSP paper: https://axllm.dev
All the Hard Stuff with LLMs in Product Development // Phillip Carter // MLOps Podcast #170: https://youtu.be/DZgXln3v85s
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Connect with Vikram on LinkedIn: https://www.linkedin.com/in/vikramr
Timestamps:
[00:00] Vikram preferred coffee
[00:41] Takeaways
[01:05] Data Engineering for AI/ML Conference Ad
[01:41] Vikram's work these days
[04:54] Fine-tuned Model insights
[06:22] Java Script tool evolution
[16:14] DSP knowledge distillation
[17:34] DSP vs Manual examples
[22:53] Optimizing task context
[27:58] API type validation explained
[30:25] LLM value and innovation
[34:22] Navigating complex systems
[37:30] DSP code generators explained
[40:56] Exploring LLM personas
[42:45] Optimizing small agents
[43:32] Complex task assistance
[49:53] Wrap up
