{"version":"1.0","type":"card","id":"65523858-c0a0-4d3e-8787-d32979d243e8","url":"https://stacklist.com/card/65523858-c0a0-4d3e-8787-d32979d243e8","title":"Saving Tokens: The Art of Efficient AI Conversations — Nerd Level Tech","source_url":"https://nerdleveltech.com/saving-tokens-and-optimizing-prompts-the-art-of-efficient-ai-conversations","note":"Packed with actionable techniques to reduce token usage without hurting output quality — prompt trimming, summarization strategies, and conversation pruning. Useful for anyone watching costs or hitting context limits in long sessions.","image":{"url":"https://ucarecdn.com/0364fa5c-49c8-4d4a-bc1f-f65f3762a267/","alt":"Saving Tokens: The Art of Efficient AI Conversations — Nerd Level Tech","width":1280,"height":800},"stack":{"id":"e16dcdcb-06b9-481c-aef9-40b6d73a5c8e","title":"Master AI & LLM Context: From Basics to Production","url":"https://stacklist.com/c/technology/stack/e16dcdcb-06b9-481c-aef9-40b6d73a5c8e"},"created_at":"2026-07-02T10:09:04.003Z","updated_at":null,"aco":{"summary":"Saving Tokens and Optimizing Prompts is a tutorial covering techniques for reducing token usage in LLM interactions, including compression, structured prompting, context caching, and smart truncation. It explains tokenization fundamentals, cost economics, and provides practical code examples using tools like OpenAI's tiktoken to measure and manage token budgets in production systems.","tags":["token-optimization","prompt-engineering","llm","cost-efficiency","tokenization","openai","ai-workflows"],"key_entities":[{"name":"OpenAI","type":"organization","confidence":0.98},{"name":"Anthropic","type":"organization","confidence":0.9},{"name":"GPT-4","type":"technology","confidence":0.95},{"name":"GPT-4-Turbo","type":"technology","confidence":0.9},{"name":"Claude","type":"technology","confidence":0.85},{"name":"tiktoken","type":"technology","confidence":0.95},{"name":"tokenization","type":"concept","confidence":0.97},{"name":"Byte Pair Encoding","type":"concept","confidence":0.9},{"name":"prompt optimization","type":"concept","confidence":0.97},{"name":"context caching","type":"concept","confidence":0.8},{"name":"Python","type":"technology","confidence":0.85}],"classification":"tutorial","language":"en","confidence":0.85,"provenance":{"model":"claude-opus-4-6","tool":"@stacklist/mcp-server@2.0.0","confidence":0.85,"timestamp":"2026-07-02T10:09:14.938Z"},"token_counts":{"approximate":3094,"cl100k":2555},"content_hash":"sha256:426733450c2854e018429469d4668649527271c979682ddba562d236f68fc9c7","acp_version":"0.2","body_available":true,"body_tokens":3094,"visibility":"public","agent_accessible":true,"status":"final"},"_links":{"self":"/api/public/card/65523858-c0a0-4d3e-8787-d32979d243e8.json","html":"https://stacklist.com/card/65523858-c0a0-4d3e-8787-d32979d243e8","md":"/api/public/card/65523858-c0a0-4d3e-8787-d32979d243e8.md","stack_json":"/api/public/stack/e16dcdcb-06b9-481c-aef9-40b6d73a5c8e.json"}}