The article titled "Skill or MCP Server? Ask What Stops the LLM From Disobeying" discusses the concept of spec-driven test automation, specifically in the context of part 4 of a series. It explores how to prevent large language models from acting against intended instructions. The piece was originally published on Towards Data Science.
The article "How to Fine-Tune an LLM: An End-to-End Guide" offers a practical, hands‑on walkthrough for fine‑tuning large language models in real‑world scenarios. It covers the entire process from data preparation to deployment, providing readers with actionable steps to adapt LLMs to specific tasks. The guide is aimed at practitioners looking to implement fine‑tuning in a structured, end‑to‑end manner.
The article titled "Towards Spec-Driven Test Automation: Part 2" discusses the implications of a single test run and what it actually proves. It appears on the Towards Data Science platform.
arXiv:2608. 08467v1 Announce Type: new Abstract: The Model Context Protocol (MCP) standardizes how servers expose data and tools to Large Language Models (LLMs).
The article titled "What's in the Box? Distinguishing Acceptable Traces from Unacceptable Ones" discusses how to transform scattered requirements into deployable agents. It focuses on identifying which traces are acceptable and which are not for effective deployment. The piece was originally published on Towards Data Science.
The article recounts a production incident where a large language model (LLM) was used to evaluate the outputs of another LLM, and the judging model consistently agreed with itself. It explores the implications of relying on one model to assess another’s work, highlighting the potential pitfalls of such an approach. The narrative offers lessons on the limits of trusting automated evaluation systems in real‑world deployments.
The article "AI Harms and Their Technical Causes" discusses how specific technical decisions in AI development can result in safety failures. It examines the underlying causes of these harms and offers guidance on alternative approaches to mitigate risks. The piece highlights the importance of thoughtful design choices in preventing unintended negative outcomes from AI systems.
Enterprise Document Intelligence [Vol. 1 #6bis] - Ask one focused clarification, learn the default from the answer, stay silent next time The post When RAG Users Ask Vague Questions: Clarify Once, Learn the Default appeared first on Towards Data Science .
A detailed look at MCP that turned my scattered tool definitions into a stable, discoverable server The post The Protocol That Cleaned Up Our Agent Architecture appeared first on Towards Data Science .
The article discusses how the reliability mechanisms added to large language model (LLM) pipelines can lead to confident but incorrect outputs, especially when the correct answer is absent. It examines the behavior of pipelines in such scenarios and highlights the paradox where safeguards intended to improve accuracy may actually reinforce errors. The piece underscores the importance of understanding pipeline responses when faced with missing or ambiguous information.
Understanding ow LLMs interact with the world around them, from returning data to taking action The post Tool Calling, Explained: How AI Agents Decide What to Do Next appeared first on Towards Data Science .
The article discusses a specialized LLM inference runtime designed for real-time applications, such as a 33 ms robot control cycle. Unlike typical runtimes that ignore physical deadlines, this system refuses new requests when the deadline is at risk, evicts key‑value cache entries based on meaning rather than age, and is implemented entirely in hand‑written CUDA without relying on cuBLAS or libtorch.
The article explains how the five core assumptions of MLOps monitoring are violated when agents are deployed to production, leading to inherited signals that incorrectly mark failed runs as healthy. It highlights the specific ways in which agent-based systems disrupt traditional monitoring stacks and the implications for reliability and performance. The piece serves as a warning for practitioners transitioning from MLOps to AgentOps, outlining the critical monitoring gaps that arise.