Question 1

The LLM is reading full JSON order payloads every turn

Accepted Answer

Past-orders and order-detail tools return raw JSON of dozens of fields. The model re-reads, re-summarises, and burns tokens on every subsequent turn. A thin server-side normaliser cuts that cost by ~75% per tool call.

Question 2

Buttons and map-pickers are emitted by the LLM

Accepted Answer

UI element choice — button labels, option arrays, when to render the map — travels through the model as tool instructions and tool outputs. Tokens pay for UI twice: to instruct the model and to retry when it skips a required render. Moving UI emission into deterministic code saves tokens and makes fabricated IDs structurally impossible.

Question 3

Reorder flow re-asks data the system already has

Accepted Answer

Load size, truck spacing, quantity, address — all in the past-orders table. A well-designed reorder prefills all of them server-side in one step; a naive one turns them into four follow-up questions and four extra LLM turns.

Question 4

System prompt carries rules the code could enforce

Accepted Answer

Typical dispatch prompts carry 40–60% of their content for rules the surrounding application could enforce deterministically. Every turn pays for those rules in tokens. Stripping them is the fastest structural win.

IA para software de dispatch de hormigón