Detailed Analysis
A Reddit post in r/Anthropic details a user's frustration with verbose, essay-like outputs from what they describe as "Claude Cowork," a managed enterprise deployment of Claude controlled by their organization's IT administrators. The user reports trying multiple mitigation strategies—including a community-made "I have ADHD" plugin and a custom "laconic-mode.md" instruction file—only to find that within a handful of exchanges, the model's responses would drift back into long-form, discursive answers regardless of explicit instructions to be brief. Their eventual workaround involved discovering that Claude Code, Anthropic's command-line coding assistant, supports "hooks" that inject instructions on every output, which proved far more durable at enforcing terse responses than one-time system prompts or reference files.
The technical detail at the center of this post—that hooks fire on every single output rather than being loaded once into context—points to a deeper and increasingly well-understood challenge in working with large language models: instruction adherence degrades over the course of a long conversation as the model's own prior outputs accumulate in context and begin to dominate its stylistic behavior. The user explicitly notices this phenomenon, observing that when Claude reads back too much of its own previous "slop," it starts mimicking that verbose speaking style in subsequent turns. This is a well-documented pattern in autoregressive generation: models are strongly influenced by the statistical patterns present in their own context window, meaning a single verbose response early in a session can create a self-reinforcing feedback loop of increasingly bloated outputs. Static instructions placed once at the start of a session are comparatively fragile against this drift, while mechanisms that re-inject guidance at every turn—like Claude Code's hooks—counteract it more reliably.
This distinction matters because it highlights a growing gap between Anthropic's different product surfaces. Claude Code, aimed at developers, exposes lower-level configurability—hooks, custom instructions, fine-grained control over agent behavior—that power users can exploit to shape model behavior precisely. Claude Cowork (or similar enterprise/team-oriented products), by contrast, is often deployed through IT-managed configurations with less end-user control over system-level behavior, prioritizing consistency and administrative oversight over individual customization. The user's complaint that they'll need to "figure out how to embed" primary instructions in a managed environment underscores a common tension in enterprise AI deployment: the very controls that make a product manageable and safe at scale can simultaneously frustrate individual users who have specific, legitimate preferences about interaction style.
More broadly, this anecdote reflects a persistent theme in the AI assistant space: users across nearly every major chatbot product (ChatGPT, Claude, Gemini, etc.) regularly report frustration with unwanted verbosity, and prompt-engineering communities have generated a cottage industry of "conciseness" system prompts, browser extensions, and now hooks-based solutions to combat it. It also signals user demand for Anthropic to natively support more robust, persistent behavioral controls—akin to Claude Code's hooks—across all its products, not just the developer-focused tools. As enterprises increasingly deploy Claude through managed, IT-controlled interfaces, the tension between organizational standardization and individual customization of AI behavior is likely to become a more prominent design and product consideration for Anthropic going forward.
Read original article →