Enterprise Document Intelligence [Vol.1 #M2] - Every RAG system is built in three engineering layers stacked on one LLM call: prompt (the call itself), context (what fills the model’s window), loop (when the next call fires and when it stops). Knowing which layer you are standing on is half of building and debugging RAG

The post Prompt, Context, Loop: The Three Engineering Layers Every RAG System Is Built On appeared first on Towards Data Science.

How to use Claude to craft an outstanding resume that lands offers

The post How Claude Help Me Build My $200k+ ML Resume appeared first on Towards Data Science.

Most coding agents treat prompt construction like retrieval: gather more files, add more context, hope the model figures it out. But that approach breaks down fast. As context grows, irrelevant code competes for attention, and when the window fills, agents start compressing their own memory—often mid-task. What looks like “forgetting” is usually just degraded context. This article explores a different approach: treating prompt construction like a compiler that decides what to keep, what to reduce, and what to discard entirely.

The post Coding Agents Don’t Need Bigger Context Windows — They Need a Context Compiler appeared first on Towards Data Science.

A hybrid LLM application pattern that combines a predefined workflow with adaptive agent behavior

The post Put the Agent Inside the Workflow appeared first on Towards Data Science.

Optimise how you interact with your coding agents

The post How to Organize All of Your Coding Agent Tasks appeared first on Towards Data Science.

What it actually takes to turn a company's scattered knowledge into something an LLM can reliably use — and why the demo is 5% of the work.

The post How to Build a Context Layer and a Company Brain appeared first on Towards Data Science.

The full conjecture is stated over abstract fields, but the counterexample is a concrete 3D function that we can explain and visualize using familiar geometric ideas and a little algebra.

The post A Simplified View of the Jacobian Conjecture appeared first on Towards Data Science.

How statistical physics explains the transition from deterministic predictions to generative AI.

The post How to Decode the Temperature Parameter in LLMs appeared first on Towards Data Science.

Prompt engineering helps you write better prompts—but it doesn’t help you change them safely. This article explores a common production failure where a simple variable rename breaks every live call, and introduces a lightweight static analysis tool that treats prompts like contracts, catching breaking changes before they ship.

The post Prompt Engineering Is Solved—Prompt Management Isn’t appeared first on Towards Data Science.

Five models, sustained generation, real wall-socket energy at $0.31/kWh — and the surprise the RTX-3090 numbers predicted, only bigger.

The post How Much Does a Local LLM Actually Cost to Run? I Measured Every Watt on Apple Silicon appeared first on Towards Data Science.

Building a browser-use agent with OpenAI Agents SDK and Playwright MCP

The post How to Give an LLM Agent a Browser appeared first on Towards Data Science.

AI has transformed how organizations operate, driving unprecedented levels of productivity and innovation. However, AI adoption can be impeded by concerns...

AI has transformed how organizations operate, driving unprecedented levels of productivity and innovation. However, AI adoption can be impeded by concerns surrounding data privacy, sovereignty and how to secure data while it is in use, or during inference and engagement with AI models. NVIDIA Confidential Computing (CC) was engineered to be a secure and performant solution for the era of agentic…

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The automotive cockpit is undergoing a fundamental shift from rule-based interfaces to agentic, multimodal AI systems capable of reasoning, planning, and...

The automotive cockpit is undergoing a fundamental shift from rule-based interfaces to agentic, multimodal AI systems capable of reasoning, planning, and acting. In most vehicles on the road today, in-vehicle assistants still rely on fixed command-response patterns: interpret a phrase, trigger an action, reset. While effective for well-defined tasks, this approach doesn’t scale to modern…

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