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

=== Assistant: Ah — now we’re squarely in the practical limitations of current LLMs and table generation. ===
Ah — now we’re squarely in the practical limitations of current LLMs and table generation. Let’s analyze this carefully and systematically.

==== 1. Token-based sequential memory - The model reads and generates token by token, not “cell by cell” in a structured table sense. - Even if you show column headers, the model doesn’t have an intrinsic “table logic” layer — it doesn’t see the table as a 2D object, it sees a long sequence of tokens. ====
# Limited context window - If the table is very wide (many columns) or very long (many rows), the earlier cells may fall outside the token context by the time the model reaches later cells. - This creates token pollution or loss of context, which can lead to inconsistencies in prerequisites, outputs, or cross-column links.
# Inter-cell dependencies - Many of your columns are interdependent (prerequisites → enabler_results → spread rate, etc.). - The AI can only reason across what it currently “remembers” in the context window; if a dependency is outside memory, it may hallucinate or contradict previous content.

==== - Current naive approach: ====

<syntaxhighlight>For row in table:
    For column in row:
        AI generates cell content sequentially

</syntaxhighlight>

Problems:
# Later columns may depend on earlier columns — if memory is limited or token pollution occurs, the model might misinterpret.
# Rows may have cross-row dependencies — model might forget or mix earlier rows.
# Large tables → context window overflows → loss of previous reasoning.

: 

==== 1. Chunked row generation with summaries - After completing a row, generate a summary of that row (in concise token form). - Feed the summary back as context for future rows so the AI can reason over dependencies without including the entire previous row content. ====
# External memory / orchestrator - Keep a structured external table in your Python program. - AI generates one cell at a time → program inserts it in the table → program generates context snippet for the next AI call that includes only relevant info. - This reduces token pollution and allows linking prerequisites → enabler_results → outputs.
# Prompt engineering / explicit references - Instead of relying on AI to “remember column positions,” explicitly provide: ``<code> Row 3, Column: tech_name = "Firestarter" Previous columns: [...] Generate column: tech_output Dependencies: [...] </code>`` - The AI now has explicit table coordinates and dependencies, reducing hallucination.
# Generate columns in parallel (optional) - Some frameworks let you generate all cells of a row in one shot with a structured output (JSON). - This reduces inter-column sequential dependency errors, though the prompt becomes heavier.

==== : ====

: 

If you want, I can outline a practical workflow for generating a 35 × 800 table with your dependencies using a small-to-medium LLM, external orchestrator, and token-efficient row summaries — which would avoid token pollution and maintain inter-column logic.

Do you want me to do that?