Editing Openai/6939b23e-87cc-8001-bdd9-9546d1143ce9 (section)

=== Assistant: Exactly — this is a subtle but very important point. ===
Exactly — this is a subtle but very important point. Let’s unpack it carefully.

==== - Directly? No. The AI cannot execute code or make API calls on its own during inference. Its internal layers don’t have the capability to send commands or control the outside world. - Its “thinking” is purely token prediction — generating text one token at a time. - There is no internal process that triggers a program to run automatically. ====
'' What it ''can'' do: It can generate text that ''describes* what to do, e.g.:

<syntaxhighlight>[Missing info: Need the maximum temperature data for Category X. Fetch from Table Y.]

</syntaxhighlight>

This is exactly like leaving a note in its output.

==== - The orchestrator is a separate program/script you control. ====
* Steps:
# AI outputs a “tag” or instruction indicating missing info.
# Orchestrator parses the tag.
# Orchestrator queries the database, table, or another source.
# Orchestrator feeds that new info back into the model as part of the next token batch / next prompt.
* The AI sees the new information in context, and its forward-pass layers incorporate it seamlessly.

: 

==== 1. AI generates part of table: ====

<syntaxhighlight>tech_name: Solar Panel
description: Converts sunlight into electricity.
Era / Period: 19th century
tech_output: [MISSING: exact efficiency %]

</syntaxhighlight>
# Orchestrator sees [MISSING: exact efficiency %], queries a database:

<syntaxhighlight>Retrieved: 15-20% typical efficiency for early solar panels

</syntaxhighlight>
# Orchestrator appends the info to context:

<syntaxhighlight>tech_output: 15-20% typical efficiency for early solar panels

</syntaxhighlight>
# AI continues generation using the new info.

==== - Step 2 in your flow is simulated intelligence: - The AI appears to request info mid-generation. - In reality, it outputs a tag or instruction. - The orchestrator interprets it and feeds new info back. ====
* This is exactly how AutoGPT / BabyAGI agents handle “self-improvement” or multi-step tasks.

===== - AI cannot actively fetch data by itself. =====
* AI can signal missing info via its output.
* External program interprets that signal and provides the info.
* When the new info is fed back, the AI seamlessly continues generation, as if it “paused to get the data.”

If you want, I can draw a step-by-step diagram showing how the AI “flags missing info” and the orchestrator fetches it, which makes your table generation workflow completely clear and ready to implement.

Do you want me to do that next?