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

=== Assistant: Yes — you’re exactly describing a practical architecture that simulates mid-thinking queries without actually retrainin… ===
Yes — you’re exactly describing a practical architecture that simulates mid-thinking queries without actually retraining the AI. Let me break the whole process carefully and clearly, step by step, and answer your questions about how it works and why it isn’t retraining.

==== - The AI model’s weights never change while it’s generating tokens. ====
* What it “knows” is entirely from: - Its training data (pattern recognition) - Tokens in the current prompt / context
* Any new external information must be fed into the model as context.

: 

==== - The AI doesn’t magically know what’s in external tables. ====
* Instead, you guide it via prompt design.

===== <syntaxhighlight>Generate a table with columns: tech_name, description, era, output. =====
If you do not know a value, tag it as [MISSING: specify].

</syntaxhighlight>
* When the AI encounters something it cannot infer from its internal knowledge, it outputs:

<syntaxhighlight>tech_output: [MISSING: efficiency of solar panels in 19th century]

</syntaxhighlight>
* That “[MISSING: …]” is the signal to the orchestrator.

: 

==== Here’s how Python orchestrates it: ====
# User prompt → Python - Python sends prompt to AI model for table generation.
# AI starts generating - Token by token, using context + trained patterns. - Reaches unknown info → outputs [MISSING: …].
# Python reads the output - Detects [MISSING: …] tag. - Interprets it as a request for external data.
# Python queries source(s) - Can query: - A local table or database - Another AI model - API, knowledge base, or any function
# Python collects the information - For example: “efficiency = 15-20%”
# Python appends new info to AI context - Either as part of the next prompt or inserted inline:

<syntaxhighlight>tech_output: 15-20% efficiency
Continue generating table…

</syntaxhighlight>
# AI resumes generation - Uses the new info as if it had “paused and looked it up.”

==== Absolutely — this is exactly how multi-agent setups work: ====
* Main AI → flags missing info → Python orchestrator → secondary AI
* Secondary AI searches or computes the info
* Orchestrator collects answer
* Sends answer back to main AI
* Main AI continues token generation

: 

==== |     |     | ====
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: 

==== - AI = robot writing a table ====
* Orchestrator = human helper reading its notes and filling gaps
* Missing info = “[MISSING: …]” notes in the robot’s draft
* Secondary AI = specialized helper the human can ask for information
* Main AI just continues writing once new info is added

No weights are changed, but the AI can generate dynamic outputs incorporating new data it didn’t originally know.

If you want, I can draw a diagram showing this full cycle:
# Main AI generating
# Missing info flagged
# Orchestrator intervenes
# Secondary AI / DB queried
# Info fed back
# Main AI continues

This will make it visually clear how to implement your dynamic table generation system.

Do you want me to do that next?