---
title: "Get Better AI Answers with Context Engineering, Not Prompt Engineering"
description: "5 source-backed AI claims from Get Better AI Answers with Context Engineering, Not Prompt Engineering, with key statistics, context, and the original AI…"
url: "https://kbanc.com/claims-library/get-better-ai-answers-with-context-engineering-not-prompt-engineering"
source: "https://aiadopters.club/p/context-engineering-ai-conversations"
date: "2025-10-06"
topics: ["strategy", "tools", "implementation"]
generated: "2026-08-31"
---

# Get Better AI Answers with Context Engineering, Not Prompt Engineering

By Kamil Banc | October 6, 2025

## Claims

1. **Finite Attention Budget** (source summary): AI models have a finite attention budget depleted by every message, file, and document.
2. **One Distractor Hurts** (source summary): Adding just one distractor document reduces model performance, according to a July 2025 study.
3. **Performance Collapse Data** (source summary): Claude 3.5 Sonnet's performance fell from 29% to 3% as context grew in May 2025 testing.
4. **Context Rot Effects** (source summary): Context rot causes recall to become fuzzy and responses generic as conversations lengthen.
5. **Iterative Curation Required** (source summary): Context engineering is iterative, requiring curation of what gets passed to the model each time.

## Evidence

### Quote
> "Your prompt didn't get worse. Your context got polluted." - Kamil Banc

### Key Statistics
- **29% to 3%**: Claude 3.5 Sonnet's performance fell from 29% to 3% as context grew, per May 2025 testing.
- **18 models**: A July 2025 study testing 18 models found that adding just one distractor document reduces performance.
- **50,000 tokens**: Feeding a model 50,000 tokens of context and asking it to recall something from 20 messages back causes performance drops.

## Context
The article grounds its practitioner guidance in recent empirical research, citing Anthropic's September 2025 context engineering work, Chroma's July 2025 context rot study across 18 models, and May 2025 arXiv testing showing dramatic performance collapse. Kamil Banc translates these findings into practical rules for daily users of Claude Projects, ChatGPT, and custom GPTs who upload files and maintain long refining threads. The core methodology frames model attention as a finite budget depleted by every message, file, and knowledge-base document. Practitioners can apply this by recognizing degradation symptoms—repeated rejected suggestions, forgotten details, vaguer answers—and by deciding when to start a new chat versus continuing an old one. The guidance is most relevant to multi-hour, file-heavy workflows rather than single-prompt users.

## Source
- Original: [Get Better AI Answers with Context Engineering, Not Prompt Engineering](https://aiadopters.club/p/context-engineering-ai-conversations)
- Cite: kbanc.com/claims-library/get-better-ai-answers-with-context-engineering-not-prompt-engineering

## Primary Evidence
- [Anthropic published research in September 2025](https://www.anthropic.com/engineering/effective-context-engineering-for-ai-agents) (anthropic.com; supports claims 1, 5)
- [context rot](https://research.trychroma.com/context-rot) (research.trychroma.com; supports claims 2, 4)
- [May 2025 testing](https://arxiv.org/abs/2505.07897) (arxiv.org; supports claim 3)