Prompt Engineering Best Practices for 2025

Prompt engineering matured in 2025. Below is a concise, field‑tested set of practices that consistently improves output quality across ChatGPT, Claude, and Gemini—plus the anti‑patterns that quietly degrade results.

The GOLDEN Checklist (2025)

Use this order when crafting any important prompt.

Goal — one clear objective and success criteria
Output — required format, length, and tone
Limits — constraints (scope, sources, policy, budget, tokens)
Data — the minimum context or examples
Evaluation — rubric to verify the result
Next — ask for a follow‑up plan or alternatives

Copy‑paste template:

Goal: {{objective and success criteria}}
Output: {{format, length, tone}}
Limits: {{scope, rules, budget, tokens}}
Data: {{context, examples, sources}}
Evaluation: {{rubric or acceptance criteria}}
Next: Provide next steps or 2 alternatives if confidence < 0.7

Model‑Specific Guidance (ChatGPT, Claude, Gemini)

ChatGPT (GPT‑4o): benefits from explicit role + formatting; excels at code and structured outputs.
Claude 3: excels at long, careful reasoning; include assumptions, safety, and critique steps.
Gemini 1.5: call out multimodal inputs and math/verification; ask for citations when relevant.

Tip: keep the same GOLDEN skeleton but tweak tone and examples per model.

Anti‑Patterns to Avoid

Vague goals: “make this better” with no rubric
Context dumping: long, unstructured text without prioritization
Hidden constraints: length, tone, or data limitations are implied but not stated
One‑shot reliance: no iteration, no evaluation, no alternatives
Format drift: not specifying output structure for downstream use

Baseline → Improve → Verify (BIV) Loop

Baseline prompt (small, fast) → collect a candidate
Improve: add missing context, constraints, or examples; optionally use a framework like CRISPE
Verify: run a Chain‑of‑Verification or Self‑Consistency pass (see Advanced Techniques)

First, produce the best answer.
Then verify by checking assumptions, sources, and edge cases.
If issues are found, revise and output the corrected answer.

Copy‑Paste Best‑Practice Prompts

Executive Brief (200 words max)

Role: Strategy analyst
Goal: Executive brief on {{topic}} for {{audience}}
Output: 5 bullet insights + 3 risks + 3 next steps (<=200 words)
Limits: Avoid jargon; cite sources if used
Evaluation: Clarity(0‑5), Evidence(0‑5), Actionability(0‑5)

Technical RFC Draft

Role: Senior engineer
Goal: Draft RFC for {{feature}}
Output: Problem, Goals/Non‑Goals, Design, Risks, Migration, Open Questions
Limits: Max 900 words; no vendor lock‑in
Evaluation: Completeness, Feasibility, Risk coverage

Multimodal Analysis (Gemini)

Task: Analyze the attached image + text for {{objective}}
Output: Findings, Evidence from image, Recommendations
Limits: Be explicit about uncertainties

Measuring Quality in 2025

Define objective metrics (win rate vs baseline, token cost, time)
Use small eval sets and track regression
Add a rubric to the prompt so quality is visible in the output

FAQ

What’s the single most important best practice? Clearly define Goal and Output—most failures start there.

Do these practices work across models? Yes. Adjust examples and tone; keep structure consistent.

How often should prompts be reviewed? Monthly for high‑value flows, or when models change.

Prompt Engineering Best Practices for 2025: Patterns, Anti‑Patterns, Checklists