How to Build Self-Improving AI Systems Without RL?

Hack Session

About the session

This session provides a hands-on, engineering-focused comparison of Large Language Models (LLMs) and Small Language Models (SLMs) in real-world applications. Participants will see both models implemented side by side across two key paradigms: Retrieval-Augmented Generation (RAG) and agentic workflows.
 
We start by building a RAG pipeline with an LLM and replicate it using an SLM, comparing performance across quality, latency, cost, and consistency. The session then extends to a simple multi-agent workflow (planner-executor), evaluating both approaches on reasoning, tool usage, and robustness, along with the impact of optimizations like prompt design, fine-tuning, and memory.
 
By the end, participants will gain a practical framework for choosing between LLMs and SLMs based on use case, constraints, and scale.

Speaker

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