Prompt Optimizer
Apply the most effective prompt engineering techniques — TOON structure, system prompt reuse, context summarization, symbolic instructions, and programmatic compression.
🏗 TOON Structure: Transforms freeform text into Task → Objective → Output → Nuance blocks. LLMs follow explicit structure 40-60% more reliably, so you get better outputs with fewer retry tokens.
🔀 System / User Split: Separates reusable instructions (role, format rules, tone) into the system prompt that you send once or pin — saving identical tokens on every subsequent message in a thread.
📦 Context Compress: Detects long background paragraphs and converts them to bulleted key-points. Reduces context padding while retaining all semantically important information.
🔣 Symbolic Instructions: Replaces verbose clauses with compact symbolic notation like [fmt:md] [len:500w] [aud:general] — each symbol replaces 4-8 tokens with 1-2.
⚡ Programmatic Compression: Multi-pass algorithm removes filler phrases, collapses tautological adjectives, merges duplicate constraints, and strips hedge words — without losing instruction precision.
Full Stack: All 5 applied in sequence. Typical savings: 30–60% on verbose prompts. Critical for reducing per-call costs on GPT-4, Claude 3.5 Sonnet, and Gemini 1.5 Pro.