Engineering High-Context LLMs: Constraints and Solutions

Folks, I started writing a book - more actually some notes for myself about modern AI systems. Here's the first chapter: https://lnkd.in/d4cFySud The now famous “chat completion” API may look sophisticated at first: roles, tools, structured output, streaming. However, the core is still one stable primitive: input tokens → model inference → output tokens Everything “agentic” is built by repeating this call, with state you carry between calls. This is determined by the nature of the transformer architecture, which is stateless and provides you a way to input a context, and to generate a response.

Tools like #OpenAI’s Prism highlight how AI is evolving from a general assistant into a true workflow partner. By embedding advanced reasoning directly into complex, high-stakes work, AI is reducing friction in research, analysis, and documentation….allowing experts to focus on mission-critical thinking rather than process overhead. For #GovCon leaders, this raises an important shift: the future workforce won’t be defined by who uses #AI, but by who knows how to apply it responsibly, securely, and strategically within regulated environments. As AI becomes native to professional workflows, the real advantage will lie in talent that can pair domain expertise with AI-augmented decision-making. How are #federal contractors preparing their teams to work alongside AI — not just as a tool, but as part of the mission delivery process? 🤔

Example of embedding AI in the way we work: Providing "scientists with superpowers," OpenAI now offers AI built into the editor scientists use to generate their papers. #digitaltransformation #GenAI #futureofwork

🚀 Scientific writers, this is for you. OpenAI has launched Prism: a workspace designed specifically for scientific writing, with AI embedded directly into the document. ✍️ Writing, 🔍 revising, 📚 citations, 🧮 LaTeX, 🤝 collaboration — all in one place, with an AI that truly understands the project. Less technical friction. More time to think, analyze, and write. Scientific writing is clearly entering a new phase. Thanks OpenAI

When you look at scientific tooling, a lot of it hasn’t changed in decades. That’s why we recently launched Prism: a free, AI-native environment for scientific writing and collaboration, designed to mean less time in your editor and more time doing research. Physicist & Research Scientist Alex Lupsasca joins Kevin Weil (VP, OpenAI for Science) and Victor Powell (Product, Prism) to walk through what it looks like when GPT-5.2 works inside a LaTeX project with full paper context. You’ll see Prism: - Polish writing with reviewable edits - Generate a clean diagram from a whiteboard photo - Spin up multiple chat threads to tackle citations and math checks in parallel Explore Prism and try it on your next draft: https://prism.openai.com

This is exactly why I’m consistently impressed by DeepSeek’s research. They have a knack for taking deep, complex problems—like how LLMs waste massive compute just to “remember” basic facts—and solving them with ideas that have been around for years, just used correctly. They recently released a research a new architectural module that integrates conditional memory into Large Language Models to handle static knowledge more efficiently. Their Engram module is a perfect example. Instead of forcing the model to think just to recall static knowledge, Engram acts like a high-speed cheat sheet: instantly retrieving facts, filtering them through context-aware gating, and freeing the model to focus on actual reasoning. Simple. Elegant. Scalable. I’ve broken down why this matters, how it works, and what makes it powerful below. #DeepSeek #LLMResearch #AIArchitecture #Transformers #EfficientAI #MachineLearning #NeuralNetworks #MoE #AIResearch #ScalingLaws

Introducing JEPA for Speech, "Soft Clustering Anchors for Self-Supervised Speech Representation Learning in Joint Embedding Prediction Architectures", a novel method for learning speech representations using soft clustering and provide a fresh perspective on evaluating representations quality — improving both downstream task performance and the quality of the representation space itself. Learning representations from speech is particularly challenging because, without external grounding, models collapse: they map all inputs to the same region of the latent space, losing the ability to distinguish between speech signals entirely. Our approach, trains a soft clustering model on speech features once, freezes it, and uses its soft posteriors as an anchor throughout training. No iterative re-clustering like HuBERT or WavLM. Smaller model. Less compute. But the deeper motivation is how we evaluate what's been learned. Most work judges representations only through downstream tasks. These matter, but a model can score well while using a fraction of its capacity. We argue: look at the representation space directly — cluster entropy, temporal stability, collapse behavior. Downstream metrics show task performance. Representation analysis shows what the model learned. Paper: https://lnkd.in/gdRwd5Yp Code: https://lnkd.in/gu5KUq2c Open-source Model checkpoint to be released soon! Authors: George Ioannides, Adrian Kieback*, Judah Goldfeder*, Linsey Pang, Aman Chadha, Aaron Elkins, Yann LeCun, and Ravid Shwartz Ziv. (picture shows top 10 most populated clusters in the representation space in a 2-D UMAP projection) *Equal contribution #SpeechAI #SelfSupervisedLearning #RepresentationLearning #JEPA

Fairfield engineering is engaging with the “Authentically Human" initiative, launched at Orientation for the Class of 2029. In Fairfield Engineering's AI Lab, students work alongside faculty to decode the logic and data behind an AI decision-making program, advancing the initiative by seeing how the logic and data of an AI-decision program can shape outcomes and inform ethical, human-centered decision-making. Read more 🔗 bit.ly/4jZJyxr

Lately, I have been reflecting on how the concept of "correctness" evolves when transitioning from deterministic software to probabilistic AI systems. In deterministic software, correctness is simple: does it follow the spec? In AI systems, reliability becomes: does it behave within constraints under real-world messiness? That shift changes the job: we’re moving from writing logic -> to designing behavior. Which in practice means blending both: classic engineering discipline (security, testing, SLAs, failure modes) + probabilistic systems thinking (uncertainty, calibration, eval design, human-in-the-loop). If you’re building agentic systems today, what’s taking more of your time: code, or constraints + context + evals?