You’re Probably Wasting AI Tokens Without Realising It

2026-07-26 · Source: LLM on Medium · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering · Depth: Intermediate, quick

Summary

This article highlights that significant AI token waste often stems from making models repeatedly rediscover information, rather than solely from long prompts. It identifies common AI applications for Quality Assurance professionals, such as analyzing Jira stories and PRDs, generating test cases, and reviewing pull requests, where token inefficiency is prevalent. The author proposes seven practical strategies to optimize token usage. These include sharing only relevant context instead of entire documents, breaking large tasks into smaller ones, utilizing reusable project instructions or skills, summarizing long conversations, building automation incrementally, using AI for reasoning rather than repetitive comparisons, and avoiding resending unchanged context. The core principle emphasized is to prevent the AI model from re-learning previously established information.

Key takeaway

For Quality Assurance professionals or developers using AI assistants like Claude Code or Cursor, optimizing token usage is crucial for efficiency and cost. You should actively avoid resending unchanged context or making the AI repeatedly process the same information. Implement strategies like breaking down large tasks, using reusable project instructions, and summarizing long conversations to reduce token consumption and enhance response quality in your daily workflows.

Key insights

Avoid making AI models rediscover information to significantly reduce token waste and improve response quality.

Principles

• Don't make the model rediscover what it already knows.
• Less unnecessary context yields better responses.
• Smaller iterations improve code and save tokens.

Method

Optimize AI token usage by selectively sharing context, breaking down tasks, leveraging reusable instructions, summarizing chats, and building automation incrementally.

In practice

• Use CLAUDE.md or Cursor Rules for reusable instructions.
• Summarize 100-message conversations into bullet points.
• Share only updated files, not entire projects.

Topics

• AI Token Optimization
• Prompt Engineering
• Large Language Models
• Quality Assurance
• Claude Code
• Context Management

Best for: Prompt Engineer, Software Engineer, Machine Learning Engineer

Related on AIssential

• 7 Practical Ways to Reduce Claude Code Token Usage — Efficient Claude Code usage hinges on managing context architecture, not just individual prompt length.
• I cut my AI coding bill in half by treating Claude and Cursor like a team, not a tool — Integrating AI coding tools with shared engineering standards significantly cuts costs and improves code quality through automated reviews.
• I Cut My AI Coding Assistant’s Token Usage in Half. Here’s Exactly How — Inefficient context management, not subscription limits, drives high AI coding assistant token usage.
• Am I'm reading too much? — The extensive use of AI assistants like "Claude Code" can lead to information overload and "diminishing returns" from constant reading, necessitating workflow redesign.
• Taught Claude to talk like a caveman to use 75% less tokens. — Prompting AI models for extremely concise, "caveman"-style responses may reduce token usage but risks quality.
• 23 Tips for Smart Claude Code Token Saving and Workflow Optimization — Proactive context management and strategic tool usage are crucial for controlling Claude Code token costs.

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Editorial summary, takeaway, and curation by AIssential. Original article published by LLM on Medium.