Designing Prompt Enhancers for ChatGPT & Claude: What Actually Works

Authored by: Rutao Xu

Market forecasts for “prompt engineering” are hard to compare because analysts define the category differently. One widely cited report projects growth from about $380.12B (2024) to roughly $6.53T (2034). (Precedence Research)

The exact number matters less than the operational reality: most teams underperform on GenAI not due to weak models, but due to unmanaged prompting. In production, the biggest failures rarely come from “not enough context.” They come from unclear success criteria and slow iteration velocity.

The job of a prompt enhancer (it’s not making prompts longer)

A real prompt enhancer increases your first-pass success rate—fewer retries, fewer edits, fewer “almost right” drafts. The best enhancers don’t feel clever; they feel boring:

• One objective: a single mission the model can’t misread
• A gold sample: a concrete example of what “good” looks like
• Strict formatting: predictable structure (Markdown, JSON, or specific bullets)
• Guardrails: constraints that prevent drift and hallucinated claims

This aligns with what OpenAI and Anthropic recommend publicly: front-load instructions, separate context cleanly, provide examples, and specify output constraints. (OpenAI Help Center)

The complexity trap: why “advanced” prompts underperform

Teams often write prompts like legal contracts—dozens of if-then rules and inflated role definitions. It feels thorough, but verbosity creates contradictions. Does “be concise” mean 50 words or 500?

There’s also a technical risk often summarized as “lost in the middle.” Research on long-context usage shows models can struggle when crucial information sits in the middle of long inputs (observed in tasks like multi-document QA and key-value retrieval), with performance often stronger near the beginning or end of the context. (arXiv)

Insight: a simpler, structured prompt is easier to test, version, and improve than a black box of text.

What works: the 4 pillars of a production prompt

1) Define one objective

If you can’t describe success in one sentence, the model won’t hit it consistently.

• Avoid: “Write a blog post about AI.”
• Adopt: “Rewrite this technical brief into a 3-paragraph summary for non-technical founders, preserving all cost-related data.”

OpenAI’s own guidance repeatedly emphasizes clarity and specificity as the baseline for better results. (OpenAI Help Center)

2) The gold sample (show, don’t tell)

One good example is worth a page of adjectives. If you want a natural founder voice, don’t just ask for “warm, empathetic.” Provide 5–10 lines that already embody that voice. Anthropic explicitly recommends showing examples of what “good” looks like. (Anthropic)

A practical note from my own workflow testing: the fastest way to reduce prompt “debate time” is to replace subjective tone words with a short sample paragraph. Once you have a gold sample, disagreements become concrete: “Does it match this or not?”

3) Lock the output format

Don’t hint at structure—declare it: “Return Markdown with H2 headings and one checklist,” or “Return JSON with keys: [title, bullets, risks].” Machine-readable outputs save hours of downstream cleanup.

OpenAI and Claude docs both treat output constraints/structure as a core controllable success lever. (OpenAI Platform)

4) Hard constraints

Constraints are your reliability layer: “Don’t invent statistics.” “Don’t mention competitors.” “Avoid filler phrases like ‘in today’s digital landscape.’” Guardrails reduce drift and protect brand voice. (Anthropic)

Three takeaways from shipping prompt workflows

1. Score like QA, not taste.
   Use a rubric (Format, Factuality, Completeness, Tone, Shippability). If it fails any check, treat it as a regression—not “a bad draft.”
2. Examples reduce cost.
   A gold sample often cuts re-prompts dramatically. When the model nails structure early, token spend and human edit time drop together.
3. Velocity beats perfection.
   Ship a v1 Prompt Card fast, log failures from real cases, and patch weekly. OpenAI’s ChatGPT prompting guidance explicitly recommends iterative refinement. (OpenAI Help Center)

The solution: the Prompt Card

To turn prompting from a fragile block of text into a versioned business asset, standardize every prompt into a Prompt Card:

• Objective: one clear sentence
• Inputs: variables the user/system provides
• Output format: exact structure required
• Constraints: must-follow / avoid rules
• Gold sample: 5–10 lines of ideal output
• Evaluation rubric: 3–5 pass/fail checks

This is how AI becomes a reliable part of the production stack.

References (external links)

• Precedence Research — Prompt Engineering Market Size and Forecast (Precedence Research)
• OpenAI — Best practices for prompt engineering (OpenAI Help Center)
• OpenAI — Prompt engineering best practices for ChatGPT (OpenAI Help Center)
• Anthropic — 6 Techniques for Effective Prompt Engineering (PDF) (Anthropic)
• Claude Docs — Prompt engineering overview (Claude开发平台)
• “Lost in the Middle: How Language Models Use Long Contexts” (arXiv / TACL) (arXiv)

Author byline: Rutao Xu (TaoApex LTD) writes about prompt evaluation, prompt testing workflows, and production practices for LLM reliability.