OpenAI Publishes Prompting Guide for GPT-5.1

Good morning. It’s Monday, November seventeenth.

On today in tech history: In 1995IBM researchers introduced Envelope Searcha brand new technique to speed up large-vocabulary speech recognition. It combined A* search’s ability to look ahead with Viterbi’s reliable timing, allowing the system to drop low-probability paths early and concentrate on those that mattered. This made real-time recognition way more practical on the limited hardware of the day. It’s not a widely known milestone, however the core idea of using smart pruning to make decoding more efficient still shows up in modern speech, translation, and LLM systems.

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Today’s trending AI news stories

OpenAI publishes prompting guide for GPT-5.1 amid AMA backlash

OpenAI just dropped a brand new toolset for GPT-5.1. The brand new prompting guide gives developers precise levers to regulate tone, structure, agent personality, response length, and verbosity, supporting applications from support bots to coding assistants.

Highlights include “apply_patch,” which slashes programming errors by 35 percent with structured diffs, and a restricted “shell” interface for controlled plan-and-execute workflows. Metaprompting is front and center. GPT-5.1 can now check its own prompts for inconsistencies and suggest fixes, making complex system management cleaner. Teams migrating from GPT-4.1 or GPT-5 are advised to implement step-by-step reasoning and reflect on function outputs to avoid narrow or incomplete responses.

But not everyone’s thrilled. GPT-5.1’s recent Reddit AMA thread devolved fast. Users complained concerning the safety router, stricter filters, and automatic rerouting from GPT-4o/4.1 to GPT-5.1, which some felt made the model cold and constrained. Neurodivergent and stressed users reported disrupted workflows and lost context, highlighting the strain between safety and human-like engagement.

On features, ChatGPT can now follow custom instructions to skip em dashes, a small fix, but a telling one. It underscores the probabilistic nature of LLM instruction-following: telling the model “no em dashes” shifts token probabilities but doesn’t hard-stop them. Adjusting one behavior can ripple across outputs, the classic “alignment tax.”

OpenAI can be exploring sparse neural networks to enhance interpretability and debugging. By pruning unnecessary connections and isolating circuits answerable for specific behaviors, models turn out to be smaller and more transparent, allowing for early detection of misaligned outputs.

Finally, Altman gave a highlight to Edison Scientific’s Kosmos, framing it as a serious AI-for-science milestone. Traders are watching, with AI-linked tokens like Fetch.ai and SingularityNET moving in response. Read more.

Google accelerates AI with SRL, Gemini multi-agent, $40B Texas data centers

Researchers at Google Cloud and UCLA rolled out Supervised Reinforcement Learning (SRL), which trains smaller models on multi-step reasoning tasks by breaking problems into sequential actions and providing dense, stepwise feedback. Early results include math benchmarks at +3%, software engineering task resolution +74% over supervised fine-tuning. SRL enables smaller, cost-efficient models to tackle complex, agentic workflows while supporting curriculum-style training for higher generalization and interpretability.

Infrastructure is getting a $40 billion boost. Google will construct three AI-ready data centers in Texas by 2027, Armstrong County and Haskell County, complementing upgrades at Midlothian and Dallas. These centers will probably be air-cooled and sure hosting Nvidia HGX B300 hardware.

Gemini can be leveling up. Gemini Enterprise introduces multi-agent tournament workflows that may generate and rank ~100 ideas in 40-minute continuous runs, functioning as co-scientists or research assistants.

The creator side gets Veo 3.1 in Gemini AI video, which lets users input multiple reference images for a single prompt. The system synthesizes video and audio, improving texture, fidelity, and audio realism, constructing on Flow’s scene extension and multi-clip stitching. Together, these updates bring finer control, lifelike output, and enterprise-grade reasoning into Google’s AI ecosystem. Read more.

Musk pushes Grok 5 to 2026 as xAI doubles down on scale

Elon Musk is punting Grok 5 to early 2026, stretching the training window as xAI scales the model and the hardware behind it. The subsequent version will ship with roughly double the parameters and, in Musk’s words, should outperform every frontier model on the market. He’s even putting a ten% probability on Grok 5 hitting human-level intelligence because of recent gains in tool use, live-video understanding, and tighter integration across 𝕏, Tesla, and SpaceX systems.

The true story is the infrastructure load. xAI is burning near $1 billion a month because the Colossus data center races toward one million GPUs. Big Tech isn’t sitting still either. Meta, Alphabet, Amazon, and Microsoft are collectively firing lots of of billions into AI capex through 2026. The model race is now a compute race, and Grok 5’s delay shows how hard it’s to maintain up. Read more.