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

=== Assistant: Exactly — that’s the core practical rule for working with LLMs. ===
Exactly — that’s the core practical rule for working with LLMs. Let me summarize it clearly:

==== - Clear, structured instructions → the model can follow them reliably. - Example: “Fill in this table with 35×800 rows using these exact columns. If data is missing, mark it [MISSING]. Use consistent units and formatting.” - Result: predictable, structured output, minimal hallucinations. ====
* Vague or general instructions → the model improvises. - Example: “Make a table about technology.” - Result: creative, inconsistent, possibly incomplete or irrelevant output.

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==== 1. Limits token attention overload - If the task is huge and poorly defined, the model tries to track too many ideas → confusion. ====
# Guides pattern recognition - Structured prompts let the model map context to output reliably, instead of relying on vague associations.
# Supports iterative generation - Structured outputs (tables, JSON, step-by-step lists) make it easy to parse, validate, or feed back missing info via an orchestrator.

==== - For any non-trivial project: 1. Define the output format explicitly (columns, units, style). 2. Set scope limits (how many rows, level of detail). 3. Use tags for missing or external info. 4. Use examples / few-shot demonstrations if needed. 5. Control generation parameters (temperature, max tokens) to balance creativity vs reliability. ====

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If you want, I can make a diagram showing how prompt structure vs vagueness affects model output, illustrating why structured prompts produce predictable tables while vague prompts produce creative/chaotic results.

Do you want me to do that?