Prompt & Inference Optimization: Building Reliable AI Systems

Hack Session

About the session

Writing prompts manually is no longer enough for building robust AI applications at scale. This beginner-friendly (101) session introduces prompt and inference optimization frameworks designed to systematically improve LLM performance through programming abstractions instead of prompt hacking. We’ll explore how they help optimize prompts, reasoning chains, and model workflows automatically using metrics and feedback loops. The session covers core concepts like signatures, modules, optimizers, evaluation, and inference strategies, along with practical techniques to improve accuracy, consistency, latency, and cost. We’ll also discuss common production challenges such as hallucinations, brittle prompts, and scaling issues—ending with a live demo of building and optimizing an AI pipeline end-to-end.

Session Takeaways

Understand prompt and inference optimization

Learn fundamentals: signatures, modules, optimizers, and evaluation

Build reliable AI workflows without manual prompt engineering

Improve accuracy, consistency, latency, and cost in production systems

Get a practical foundation to start building optimized LLM applications

Speaker

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