Next-Generation Prompt Engineering: Building Scalable, Cost-Efficient AI Applications for Multi-Model and Open-Source Environments

As AI applications grow in complexity, traditional prompt engineering approaches struggle to keep pace. Many applications rely on rigid configurations and manual iterations, resulting in brittle, costly systems that are challenging to scale and adapt. This talk introduces next-generation prompt engineering, a rapidly advancing area of research that emphasizes building flexible, cost-optimized applications capable of consistent performance across diverse, including open-source, environments.

Attendees will learn how generalizable techniques are enabling teams to harness the power of open-source LLMs with greater reliability, bringing the benefits of adaptable and scalable AI to open-source communities.

Key topics will include:

• Multi-Model Consistency: Programmatic techniques for creating prompts that yield stable, dependable results across various LLMs, enabling cross-platform and open-source compatibility.
• Adaptability to Model Updates: Methods to future-proof applications, allowing smooth integration of frontier and open-source models while maintaining performance integrity.
• Synthetic Data Generation for Robust Testing and Evaluation: Leveraging synthetic data to simulate diverse scenarios, enhancing prompt resilience across proprietary and open-source LLMs alike.
• Cost Optimization through Structured Prompting: Techniques to craft prompts that produce structured, measurable outputs, optimizing model usage and reducing operational costs without sacrificing quality.
• Production Monitoring and Feedback Loops: Systems for tracking prompt performance, providing feedback that ensures application stability and cost-efficiency over time.

This session will equip developers with innovative, open-source-compatible techniques for building scalable, adaptable, and cost-effective AI applications. Attendees will leave prepared to leverage the full potential of both proprietary and open-source LLMs, creating robust AI solutions ready to meet the demands of a dynamic, multi-model landscape.