Claims Library Entry
Get Better AI Answers with Context Engineering, Not Prompt Engineering
This article explains why long AI conversations degrade in quality due to limited attention budgets and 'context rot.' It offers practical rules for managing what the model sees, deciding when to start a new chat, and setting up Projects and custom GPTs without overwhelming the model with noise.
Published October 6, 2025 by Kamil Banc
Lead claim
Your prompt didn't get worse — your context got polluted, degrading AI answer quality.
Atomic Claims
What this article supports
Copy individual claims as needed.
Claim 1 · Source summary
Finite Attention Budget
AI models have a finite attention budget depleted by every message, file, and document.
Claim 2 · Source summary
One Distractor Hurts
Adding just one distractor document reduces model performance, according to a July 2025 study.
Claim 3 · Source summary
Performance Collapse Data
Claude 3.5 Sonnet's performance fell from 29% to 3% as context grew in May 2025 testing.
Claim 4 · Source summary
Context Rot Effects
Context rot causes recall to become fuzzy and responses generic as conversations lengthen.
Claim 5 · Source summary
Iterative Curation Required
Context engineering is iterative, requiring curation of what gets passed to the model each time.
Evidence
Context behind the claims
Quote
"Your prompt didn't get worse. Your context got polluted."
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.
Supporting 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.
How to Cite
Use the claim-level citation when you need a precise statement. Use the article or claims-collection citation when you want the wider argument and source context.
Individual Claim
Best when you need to cite one atomic claim directly inside a memo, deck, research note, or AI output.
"[claim text]" (Banc, Kamil, 2025, https://kbanc.com/claims-library/get-better-ai-answers-with-context-engineering-not-prompt-engineering)Original Article
Use this when you want to cite the full newsletter article at AI Adopters Club rather than the structured claims page.
Banc, Kamil (2025, October 6, 2025). Get Better AI Answers with Context Engineering, Not Prompt Engineering. AI Adopters Club. https://aiadopters.club/p/context-engineering-ai-conversationsClaims Collection
Use this when you want to reference the full structured claims collection on this page.
Banc, Kamil (2025). Get Better AI Answers with Context Engineering, Not Prompt Engineering [Structured Claims]. Retrieved from https://kbanc.com/claims-library/get-better-ai-answers-with-context-engineering-not-prompt-engineeringAttribution Requirements
- Include the author name: Kamil Banc.
- Include the source: AI Adopters Club or the structured claims page.
- Link to the original article or the claims page you used.
- Indicate any edits or transformations if you changed the wording.
Related Reading
More from the library
A structured prompt approach transforms performance reviews into actionable development plans by interviewing managers through six categories. The method prevents common AI pitfalls by collecting complete information before generating recommendations, producing budget-aligned plans in a single session.
5 claims
A detailed analysis of 30 days of ChatGPT and Claude conversations reveals 10 repeating prompt patterns that demonstrate systematic AI use. The author shares specific prompt structures for tasks like email triage, presentation assembly, and workflow documentation, showing how to treat AI as infrastructure rather than a casual tool.
5 claims
AI adoption fails because of habit problems, not training gaps. This practical guide shows how to build an AI reflex muscle in 20 minutes by automating one annoying task. The goal is developing automatic pattern recognition for AI opportunities.
5 claims