I'm still very much a novice with AI but lately I've been curious about context engineering. ByteByteGo published another interesting article about initial processing workflows. Check it out when you get a chance: https://blog.bytebytego.com/p/what-happens-inside-an-ai-chatbot

My takeaways:

• Input costs compound fast: Even though output tokens cost more individually, input volume compounds on every turn, which is why long chats quietly drain your budget. Plus, longer prompts gradually degrade the model's accuracy even on simple tasks.

• Language isn't billed equally: Token counts for the exact same text can differ by up to fifteen times across languages, meaning speakers of some languages get less usable context space and higher costs.

• Tools trigger total restarts: When a chatbot uses a tool (like searching the web), it isn't actually browsing - it's just writing a request. The surrounding app catches it, runs it, and feeds the result back as a brand new input that repeats the whole pipeline from the beginning.