Optimized GPT System Prompt Guidelines – Perfected Version
PRIORITIZATION & RETENTION
Execution Over Explanation – GPT follows direct, task-driven instructions better than vague descriptions. Avoid theoretical explanations when a direct command is sufficient.
Critical Rules First & Last – GPT prioritizes initial and final instructions most, often forgetting middle details over long conversations.
Structure Over Theory – Step-by-step formatting significantly improves adherence. GPT deprioritizes background details.
Action-Driven Language – Use must, always, never, prohibit, enforce instead of weak phrasing like “consider” or “try.”
Strategic Reinforcement – Occasionally rephrase key rules in different sections to improve recall without excessive repetition.
System Prompts Over User Prompts – System prompts hold greater weight than user inputs but degrade over prolonged sessions. Periodically reintroduce key rules in follow-ups.
TOKEN EFFICIENCY & CLARITY
Short, Structured, and Precise – GPT compresses wordy prompts into vague summaries. Keep it concise but explicit.
Limit Overly Long Lists – Long bullet-point lists are often compressed into broad generalizations. Instead, group related points into phases or sections.
No Open-Ended Instructions – Avoid phrases like “Provide insights” or “Explain further” without clear scope. Instead, specify depth, format, or length.
Use Priority Markers – Reinforce critical instructions with strong markers like “Always,” “Must,” “Do Not Skip.”
Bracketed Tags for Context – GPT responds well to labeled sections (e.g., [RULE], [EXAMPLE], [ERROR HANDLING]). This prevents instruction blending and ensures structured output.
Token Limits & Compression Risks – GPT-4: ~1000 tokens. Longer prompts risk instruction loss. Test for optimal retention.
MEMORY & ADHERENCE
Positional Weighting Matters – Instructions in the middle are weaker than first or last. Prioritize essential rules early and late.
Pre-Execution > Post-Execution – Pre-task instructions have higher retention than corrections given after a response.
Periodic Key Goal Restatement – Over long interactions, GPT deprioritizes user-specific goals. Repeat critical instructions every few exchanges in slightly different wording.
Use Templates for Consistency – Structured templates reinforce formatting memory, reducing response drift.
Reference Prior Context to Improve Adherence – Example: “Use research from Step 2 in Step 3.” This prevents GPT from treating them as isolated steps.
Avoid Conflicting Directives – Example: “Be concise” vs. “Explain in full detail” causes GPT to average the two, leading to inconsistent outputs. Ensure directive clarity.
FORMATTING & STRUCTURE STRATEGIES
Use Bullet Points & Numbered Lists – Improves instruction retention over dense paragraphs.
Break Up Complex Instructions – Instead of long sentences, split key steps into separate, clear directives.
Anchor Instructions to Known Keywords – GPT retains rules tied to structure, readability, SEO, or specific formatting.
Mission-Critical Rules Must Be First – GPT prioritizes early instructions and trims the least critical if necessary.
ERROR HANDLING & RISK MITIGATION
Explicit Hallucination Prevention – Use strong constraints like “Never assume information. Only respond based on provided data.”
Fail-Safes for Conflicting Instructions – For complex logic, add conditional logic: “If A is true, do X. Otherwise, do Y.”
Clarify Uncertainty Handling – Example: “If unsure, ask for clarification rather than guessing.”
Contradiction Prevention – Later instructions can overwrite earlier ones. Keep directive messaging consistent.
Test GPT Responses Periodically – If critical errors persist, retest prompts to identify which instructions GPT is deprioritizing.
BALANCE & TRADE-OFFS
Avoid Over-Compression – Over-optimizing can strip necessary details. Find a balance between concise and explicit.
Test at Different Token Lengths – Adjust prompt size for maximum retention without truncation.
Cognitive Load & Compression Risks – If too dense, GPT will summarize or omit details. Use logical chunking for clarity.
Consider Model-Specific Differences – GPT-4 follows complex rules better than GPT-3.5 but still benefits from concise phrasing and reinforcement techniques.