Switch commands for AI

 Here are 20 powerful switch commands and parameters to enhance ChatGPT's analytical depth, precision, and insight generation:

Core Control for Response Quality

• --temperature <value> → Adjusts creativity (0 = strict, 1 = highly creative).
• --top_p <value> → Controls diversity of responses (lower values give more focused results).
• --max_tokens <value> → Limits response length for concise answers.
• --stop <text> → Defines stopping conditions to prevent unnecessary output.
• --response_format json → Structures output in JSON for better parsing and automation.

Depth & Insight Enhancement

• --logprobs <N> → Returns probabilities of token choices for better analysis.
• --n <value> → Generates multiple completions for comparative insights.
• --presence_penalty <value> → Boosts novelty by encouraging new ideas.
• --frequency_penalty <value> → Reduces overused phrases for more varied responses.
• --best_of <N> → Returns the best completion out of multiple attempts.

Analytical & Logical Rigor

• --system "<directive>" → Sets explicit system instructions to focus on logic, factual accuracy, or a particular analysis style.
• --logit_bias {token_id: value} → Prioritizes or suppresses specific words for fine-tuned responses.
• --seed <value> → Ensures repeatability for controlled testing and comparisons.
• --debug → Provides AI decision-making reasoning and internal weighting.

Data-Driven & Computational Boost

• --stream → Enables real-time responses for interactive discussions.
• --tool_use <name> → Forces use of external tools like Python or APIs for deeper computation.
• --multi-turn <true> → Enhances memory across a session for complex multi-step analysis.
• --context_length <value> → Extends recall capacity for handling longer documents or discussions.
• --expand_references → Ensures citations, sources, or footnotes are included in responses.

Would you like real-world examples of these in action? 🚀