NemoClaw Knowledge Wiki

prompt-engineering

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Prompt Engineering

Core Philosophy

Prompt engineering emphasizes optimizing the surrounding AI Agent Infrastructure and orchestration to achieve performance gains that often exceed those of underlying model upgrades. This approach prioritizes the Harness Architecture and Context Language Models over raw parameter count.

Key Developments

Jev: Rapid Structured Decision-Making

  • Overview: Jev is a frontier model designed for fast, structured decision-making in software applications Jev: Frontier Model for Rapid, Structured Decision-Making.
  • Nature: It refines and extends existing classification and decision models, particularly in its ability to follow dynamic instructions, rather than introducing a new model category.
  • Integration: Focuses on System-1 style rapid processing within the agent harness.
  • Source: OPEN JEVs are Here!

Model Landscape & Deployment

  • Qwen 3.8-27B: Emerged as a high-performance open-source multimodal model.
  • Local Deployment: Runs efficiently on consumer hardware via llamacpp and the DeepSeek harness.
  • Benchmark Integrity: Critical analysis of reported scores for models like gpt-6-astra reveals potential integrity issues in current benchmarks.

Source Notes

Graph View

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