Prompt Engineering

When AI agents begin to talk to themselves—really talk to themselves—we might just witness a shift in how machine reasoning is conceived. A new paper, “Introspection of Thought Helps AI Agents”, proposes a reasoning framework (INoT) that takes inspiration not from more advanced outputs or faster APIs, but from an old philosophical skill: inner reflection. Rather than chaining external prompts or simulating collaborative agents outside the model, INoT introduces PromptCode—a code-integrated prompt system that embeds a virtual multi-agent debate directly inside the LLM. The result? A substantial increase in reasoning quality (average +7.95%) and a dramatic reduction in token cost (–58.3%) compared to state-of-the-art baselines. Let’s unpack how this works, and why it could redefine our mental model of what it means for an LLM to “think.” ...

Large Language Models (LLMs) are increasingly deployed as synthetic survey respondents in social science and policy research. But a new paper by Rupprecht, Ahnert, and Strohmaier raises a sobering question: are these AI “participants” reliable, or are we just recreating human bias in silicon form? By subjecting nine LLMs—including Gemini, Llama-3 variants, Phi-3.5, and Qwen—to over 167,000 simulated interviews from the World Values Survey, the authors expose a striking vulnerability: even state-of-the-art LLMs consistently fall for classic survey biases—especially recency bias. ...

When Meta and other tech giants scale back content moderation, the gap isn’t just technical—it’s societal. Civil society organizations (CSOs), not corporations, are increasingly on the frontlines of monitoring online extremism. But they’re often armed with clunky tools, academic prototypes, or opaque black-box models. A new initiative—highlighted in Civil Society in the Loop—challenges this status quo by co-designing a Telegram monitoring tool that embeds human feedback directly into its LLM-assisted classification system. The twist? It invites civil society into the machine learning loop, not just the results screen. ...

If you’re running a GenAI-powered application today, you’re likely sitting on a ticking time bomb. It isn’t your codebase or infrastructure — it’s your prompts. As Large Language Models (LLMs) evolve at breakneck speed, your carefully tuned prompts degrade silently, causing once-reliable applications to behave erratically. The case of Tursio, an enterprise search tool, makes one thing painfully clear: prompt migration is no longer optional — it’s survival. The Hidden Cost of Progress In 2023, Tursio ran reliably on GPT-4-32k. By mid-2025, it had to migrate twice — first to GPT-4.5-preview, then to GPT-4.1. Each model came with its own quirks: ...

Large language models (LLMs) have graduated from being glorified autocomplete engines to becoming fully-fledged agents. They write code, control mobile devices, execute multi-step plans. But with this newfound autonomy comes a fundamental problem: they act—and actions have consequences. Recent research from KAIST introduces Causal Influence Prompting (CIP), a method that doesn’t just nudge LLMs toward safety through general heuristics or fuzzy ethical reminders. Instead, it formalizes decision-making by embedding causal influence diagrams (CIDs) into the prompt pipeline. The result? A structured, explainable safety layer that turns abstract AI alignment talk into something operational. ...

In the quest for truly intelligent systems, reasoning has always stood as the ultimate benchmark. But a new paper titled “Towards a Deeper Understanding of Reasoning Capabilities in Large Language Models” by Annie Wong et al. delivers a sobering message: even the most advanced LLMs still stumble in dynamic, high-stakes environments when asked to reason, plan, and act with stability. Beyond the Benchmark Mirage Static benchmarks like math word problems or QA datasets have long given the illusion of emergent intelligence. Yet this paper dives into SmartPlay, a suite of interactive environments, to show that LLMs exhibit brittle reasoning when faced with real-time adaptation. SmartPlay is a collection of dynamic decision-making tasks designed to test planning, adaptation, and coordination under uncertainty. The team evaluates open-source models such as LLAMA3-8B, DEEPSEEK-R1-14B, and LLAMA3.3-70B on tasks involving spatial coordination, opponent modeling, and planning. The result? Larger models perform better—but only to a point. Strategic prompting can help smaller models, but also introduces volatility. ...

Cognaptus Insights introduces Retrieval-Augmented Learning (RAL), a new approach proposed by Zongyuan Li et al.¹, allowing large language models (LLMs) to autonomously enhance their decision-making capabilities without adjusting model parameters through gradient updates or fine-tuning. Understanding Retrieval-Augmented Learning (RAL) RAL is designed for situations where fine-tuning large models like GPT-3.5 or GPT-4 is impractical. It leverages structured memory and dynamic prompt engineering, enabling models to autonomously refine their responses based on previous interactions and validations. ...

Generative AI is often praised for its creativity—composing symphonies, painting surreal scenes, or offering quirky new business ideas. But in some contexts, especially research and data processing, consistency and accuracy are far more valuable than imagination. A recent exploratory study by Utrecht University demonstrates exactly where Large Language Models (LLMs) like Claude 3 Opus shine—not as muses, but as meticulous clerks. When AI Becomes the Analyst The research project explores three different use cases in which generative AI was employed to perform highly structured research data tasks: ...

“I think the next year’s Turing test will truly be the one to watch—the one where we humans, knocked to the canvas, must pull ourselves up… the one where we come back. More human than ever.” — Brian Christian (author of The Most Human Human) The AI Masquerade: Why Personality Now Wins the Game Artificial intelligence is no longer confined to tasks of logic or data wrangling. Today’s advanced language models have crossed a new threshold: the ability to convincingly impersonate humans in conversation. A recent study found GPT-4.5, when given a carefully crafted prompt, was judged more human than actual humans in a Turing test (Jones & Bergen, 2025). This result hinged not simply on technical fluency, but on the generation of believable personality—a voice that shows emotion, adapts to social context, occasionally makes mistakes, and mirrors human conversational rhythms. ...

📺 Watch this first: Jimmy O. Yang on “Guess How Much” “Because the art is in the savings — you never pay full price.” 💬 “Guess How Much?” — A Philosophy for AI Developers In his stand-up comedy, Jimmy O. Yang jokes about how Asian families brag not about how much they spend, but how little: “Guess how much?” “No — it was $200!” It’s not just a punchline. It’s a philosophy. And for developers building LLM-powered applications for small businesses or individual users, it’s the right mindset. ...