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The paper proposes metrics like Override Success Rate (OSR) and alignment inertia to audit the durability of prior training influences on LLM behavior when operators attempt policy overrides through prompting or fine-tuning.
The article discusses how to use system prompts to make AI models behave like specific personas, providing a step-by-step guide with examples like 'John Llama' to illustrate techniques for creating humanlike chatbot interactions.
Thariq from Anthropic mentions upcoming improvements to make Claude Code more hackable, including system prompt modifications, with further details to be shared soon.
This article explains how reasoning traces work in AI models, discussing their implementation, concealment, and extraction techniques, with examples from models like GPT-OSS and DeepSeek.
Anthropic publishes system prompts for its Claude models, such as Claude Fable 5 and Opus 5, through dated release notes to reveal changes in production prompts.
Release notes for system prompts in Claude, detailing updates or new features for the AI model's functionality.
The author shares principles for writing effective prompts for AI agents, emphasizing focusing on what truly matters, high-signal communication, actionable instructions, and using established phrasing.
Jerry Liu discusses the counterproductive effects of over-constraining AI models like Claude Code with extensive prompts, and predicts that deference to model judgment will increase.
Anthropic details new rules for context engineering with Claude 5, emphasizing letting the model use judgment over rigid rules, designing interfaces instead of giving examples, using progressive disclosure, and simplifying tool descriptions.
This article discusses Anthropic's findings on prompt simplification, as well as Maka Agent's verification results on Terminal Bench 2.1, showing that overly verbose system prompts actually degrade performance under stronger models.
Anthropic shares lessons from removing 80% of the Claude Code system prompt for new Claude models, advising users to let the models use judgment over strict rules and to update context engineering practices.
Maxime Rivest shares tips for optimizing Pi coding agent usage with custom system prompts, and Matei Zaharia discusses surprising results from benchmarking coding agents at Databricks.
This article details DexOS, a novel AI agent architecture that uses a local cryptographic governance runtime and state machine to prevent agent drift, rejecting reliance on system prompts and RLHF.
Using the DSPy framework, the author evaluates and improves the system prompts for Datasette Agent's SQL query feature, identifying issues such as column-name guessing and error-retry loops.
The author speculates that cloud chatbots like ChatGPT and Claude appear less intelligent than local open models due to system prompts that impose a personality, and wonders if using raw APIs mitigates this.
This paper presents a multi-dimensional analysis of human-like behaviors in LLMs, examining prevalence, effects, and controllability across 21,000 conversations from four models, finding that behaviors vary by model and user factors, with implications for responsible design.
SePO (Self-Evolving Prompt Optimization) proposes a self-referential prompt agent that optimizes both task agents' system prompts and its own system prompt through an evolutionary search, outperforming Manual-CoT, TextGrad, and MetaSPO across five benchmarks including AIME'25, ARC-AGI-1, and GPQA.
A researcher shares an observation in evaluating subagent behavior within deep agent systems, noting an interesting quirk in how agents align with hand-written system prompts versus instructions from the orchestrator.
This article presents a comprehensive guide to reduce token costs in Agentic AI systems by 95%, detailing seven core techniques including tree-structured document architecture, AI auto-compression, local model management, and script-to-API calls.
The article discusses how Addy Osmani argues that the performance difference between AI coding agents like Claude Code, Cursor, and Cline stems from their 'Harness'—the layer of prompts, tools, and constraints around the model—rather than the underlying model itself. It details best practices for harness engineering, including hooks, sandboxing, and context management, to bridge the gap between model capability and actual agent performance.