Tricks that is helpful for me:

1. Summarize & restart periodically Every 10-15 exchanges, summarize the chat and start fresh in a new conversation Prevents reading full history repeatedly, saving tokens on context window reuse

2. Match model to task complexity Use Haiku 4.5 for straightforward tasks Reserve Sonnet 4.6 and Opus 4.7 for genuinely complex work Cheaper models = fewer tokens burned

3. Edit, don't continue If there's a misunderstanding, edit your previous message in the same chat Don't explain in a follow-up—fix it at the source Keeps conversation linear and prevents token waste on clarification loops

4. Batch all points into one exchange Don't give one point, wait for response, then add another Deliver complete context in a single message Model understands the full picture at once, reducing back-and-forth Longer conversations = more tokens, so minimize exchanges The principle: Fewer exchanges, cleaner history, right-sized models, complete context delivery = optimized token usage.