---
title: "Why Your GPT-5 Outputs Are Shallow or Bloated (and how to fix it)"
description: "5 source-backed AI claims from Why Your GPT-5 Outputs Are Shallow or Bloated (and how to fix it), with key statistics, context, and the original AI Adopters…"
url: "https://kbanc.com/claims-library/why-your-gpt-5-outputs-are-shallow-or-bloated-and-how-to-fix-it"
source: "https://aiadopters.club/p/gpt-5-reasoning-depth-output-scope"
date: "2025-10-01"
topics: ["tools", "implementation"]
generated: "2026-08-31"
---

# Why Your GPT-5 Outputs Are Shallow or Bloated (and how to fix it)

By Kamil Banc | October 1, 2025

## Claims

1. **Precision Prompting Shift** (source summary): GPT-5 follows precise instructions better than previous models but performs worse when prompts are vague.
2. **Router Controls Two Dials** (source summary): GPT-5 routes every request through a router that independently selects reasoning depth and output verbosity.
3. **Cost-Optimized Defaults** (source summary): The router defaults to low reasoning and medium verbosity because those settings are cheapest to run.
4. **Reasoning Trigger Phrases** (Kamil's interpretation): Explicit phrases like 'Think hard about this' force high reasoning, while vague emphasis like 'This is critical' fails.
5. **Explicit Output Boundaries** (Kamil's interpretation): Setting exact word counts, such as 100 words or 600 to 800 words, controls verbosity.

## Evidence

### Quote
> "GPT-5 flipped this. It follows precise instructions better than any previous model but performs worse when you're vague." - Kamil Banc

### Key Statistics
- **100 words or less**: Recommended compressed output length for executive updates that senior people scan in under 30 seconds
- **3 to 5 paragraphs**: Recommended standard briefing length for team coordination where colleagues need causation context
- **600 to 800 words**: Recommended comprehensive documentation length for reference materials used by multiple teams over weeks

## Context
The article presents a practitioner framework based on Kamil Banc's analysis of GPT-5's architectural changes, specifically its router system that makes independent decisions about reasoning depth and verbosity. The methodology relies on testing specific instruction phrases against the router's cost-optimized defaults, distinguishing between explicit cognitive commands that work and vague emphasis phrases that fail. Practitioners can apply this by adding reasoning triggers like 'Think hard about this' for high-stakes decisions such as financial planning or risk evaluation, while specifying exact word counts matched to audience needs. The author acknowledges a trade-off: forcing higher reasoning increases processing time and token cost, so it should be reserved for situations where getting it wrong once costs more than the compute. The framework is framed as practitioner experience rather than peer-reviewed research, so readers should verify results against their own use cases.

## Source
- Original: [Why Your GPT-5 Outputs Are Shallow or Bloated (and how to fix it)](https://aiadopters.club/p/gpt-5-reasoning-depth-output-scope)
- Cite: kbanc.com/claims-library/why-your-gpt-5-outputs-are-shallow-or-bloated-and-how-to-fix-it