I have been extensively working with prompt engineering techniques lately, and the methods I have learnt have fundamentally changed how I interact with large language models. Here is what has been particularly effective: Recursive Expansion for Comprehensive Coverage - I embed instructions within my prompts that direct the model to expand topics recursively. This ensures the AI automatically explores subjects in depth without requiring multiple follow-up queries.​
Maximising Token Window Utilisation (99.99% Usage) - I strategically utilise nearly the full context window to circumvent rate limiting and avoid truncation issues. This results in more comprehensive outputs without mid-response cutoffs.​
Applying the DRY Principle (Don't Repeat Yourself) - I structure prompts to eliminate redundancy. This keeps responses focused and allocates tokens more efficiently towards meaningful content.​
Internal Monologue for Enhanced Transparency - I request AI to articulate its reasoning process before providing final outputs. This transparency enables early identification of potential errors.​
360-Degree Thinking for Holistic Analysis - I instruct the model to dynamically identify and analyze all relevant perspectives based on the topic. This ensures comprehensive coverage across all applicable dimensions.​
Visual Aids Through ASCII Mindmaps and ASCII Decision Charts - Incorporating ASCII-based diagrams has significantly improved information accessibility without requiring external visualisation tools.
Ultra-Verbosity for In-Depth Understanding - For scenarios requiring thorough explanations, I request ultra-verbose responses with extensive context and examples. This proves particularly valuable when surface-level answers are insufficient.​
Persona-Based Emulation - I incorporate personas of established authors or thought leaders into prompts. This significantly alters the writing style and makes technical content more engaging.
Fact-Checking to Avoid Hallucinations - I explicitly instruct models to verify their claims and cite sources wherever possible. Grounding responses in verifiable data ensures reliability.​
Generating Follow-Up Questions for Rabbit Hole Learning - I instruct the model to provide 10 relevant follow-up questions at the end of each response. This creates a rabbit hole-style learning experience for deeper exploration.​ Recursive Expansion for Comprehensive Coverage - I embed instructions within my prompts that direct the model to expand topics recursively. This ensures the AI automatically explores subjects in depth without requiring multiple follow-up queries.​ Maximising Token Window Utilisation (99.99% Usage) - I strategically utilise nearly the full context window to circumvent rate limiting and avoid truncation issues. This results in more comprehensive outputs without mid-response cutoffs.​ Applying the DRY Principle (Don't Repeat Yourself) - I structure prompts to eliminate redundancy. This keeps responses focused and allocates tokens more efficiently towards meaningful content.​ Internal Monologue for Enhanced Transparency - I request AI to articulate its reasoning process before providing final outputs. This transparency enables early identification of potential errors.​ 360-Degree Thinking for Holistic Analysis - I instruct the model to dynamically identify and analyze all relevant perspectives based on the topic. This ensures comprehensive coverage across all applicable dimensions.​ Visual Aids Through ASCII Mindmaps and ASCII Decision Charts - Incorporating ASCII-based diagrams has significantly improved information accessibility without requiring external visualisation tools. Ultra-Verbosity for In-Depth Understanding - For scenarios requiring thorough explanations, I request ultra-verbose responses with extensive context and examples. This proves particularly valuable when surface-level answers are insufficient.​ Persona-Based Emulation - I incorporate personas of established authors or thought leaders into prompts. This significantly alters the writing style and makes technical content more engaging. Fact-Checking to Avoid Hallucinations - I explicitly instruct models to verify their claims and cite sources wherever possible. Grounding responses in verifiable data ensures reliability.​ Generating Follow-Up Questions for Rabbit Hole Learning - I instruct the model to provide 10 relevant follow-up questions at the end of each response. This creates a rabbit hole-style learning experience for deeper exploration.​ Impact on Workflow: These techniques represent a fundamental shift in how I approach problem-solving with AI. The result is higher quality outputs, fewer iterations, and substantially greater control.​ What prompt engineering methods have proved effective in your experience? Feel free to share your thoughts.

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