context-minimization-pattern.md

title	Context-Minimization Pattern
status	emerging
authors	Luca Beurer-Kellner et al. (2025)
category	Context & Memory
source	https://arxiv.org/abs/2506.08837
tags	context-hygiene taint-removal prompt-injection

Problem

User-supplied or tainted text lingers in the conversation, enabling it to influence later generations.

Solution

Purge or redact untrusted segments once they've served their purpose:

• After transforming input into a safe intermediate (query, structured object), strip the original prompt from context.
• Subsequent reasoning sees only trusted data, eliminating latent injections.

sql = LLM("to SQL", user_prompt)
remove(user_prompt)              # tainted tokens gone
rows = db.query(sql)
answer = LLM("summarize rows", rows)

How to use it

Customer-service chat, medical Q&A, any multi-turn flow where initial text shouldn't steer later steps.

Trade-offs

• Pros: Simple; no extra models needed; helps prevent context window anxiety by reducing overall context usage.
• Cons: Later turns lose conversational nuance; may hurt UX; overly aggressive minimization can remove useful context.

References

• Beurer-Kellner et al., §3.1 (6) Context-Minimization.
• Building Companies with Claude Code - Emphasizes importance of eliminating context contradictions: "if there's any contradictions in your prompt, you're going to receive lower quality output"