prompting
Beyond Market Intelligence keeps prompting in one place: 4 stories so far. The section currently leads with “EvoUndo gives AI agents the power to evolve safely without losing control.”, “Design Your Data: Why a Simple FAQ Can Outperform RAG”, and “Where AI learns the physics of teamwork and competition.”. Self-modifying agents are risky. Most RAG setups fight messy documents. Acme AI is the next-generation, AI-powered spreadsheet platform built to replace Excel and redefine how analysts, data scientists, and enterprise teams work… The list below is every prompting story on Beyond Market Intelligence, newest first.
EvoUndo gives AI agents the power to evolve safely without losing control.
Self-modifying agents are risky. EvoUndo, a new framework, shows that a successful mutation can leave persistent effects that are unsafe to reverse. Testing 600 unseen tasks, it found 197 capability-improving mutations that failed recoverability verification. This research reveals that fixing these failures requires more than just iterative prompting. It demands co-designing verification, state grounding, and recovery-language expressivity. We appreciate how this work reframes the problem, moving beyond simple repair strategies toward a more principled approach.
Design Your Data: Why a Simple FAQ Can Outperform RAG
Most RAG setups fight messy documents. This piece flips that struggle into a design choice. A well-structured FAQ inverts the pipeline: parsing becomes trivial, retrieval acts as a cache, and few-shot prompting turns into a retrieval problem. It's a clever reframing that rewards intentionality. If you're tired of wrestling with chaotic corpora, this perspective offers a practical path forward. For a deeper look at how LLMs handle structure, our piece on paragraph navigation in token space pairs well with this.
Where AI learns the physics of teamwork and competition.
The Agentic World Cup asks a sharp question: what happens when LLMs stop crunching numbers and start playing soccer? The team behind it sees sports as the proving ground for embodied intelligence, a space where agents must react, adapt, and outmaneuver in real time. It's a clever reframing of the "embodiment gap," and the format is refreshingly direct: pick a model, coach it through prompts, and let it compete.
Explore how AI sees Trump before context enters the picture
A single word, "Trump," behaves like two different neighbors depending on how you slice its embedding. In GPT-2 Small's static table, discretizing the coordinates surfaces generic political names like Mitt and Pelosi. Keep the continuous values, and the same token clusters with family, staff, and rivals like Obama and Eisenhower. That gap is a quiet reminder that representation choices shape what models see.