Prompt Engineering Best Practices (2026) – Checklist & Copy-Paste Templates | Prompt Builder

Prompt engineering didn’t become “writing longer prompts” in 2026. It became writing clearer specs.

This guide is a practical, copy‑paste set of best practices you can use for ChatGPT, Claude, and Gemini, including templates for system prompts, structured requests, and quick self-check evals.

If you’re looking for the prior edition, see Prompt Engineering Best Practices for 2025.

The 2026 Checklist (Use This Every Time)

Success criteria: What does “done” look like (and how will you judge it)?
Output contract: Format, length, tone, and required sections (make it testable).
Constraints: Scope, assumptions, exclusions, and what to do when uncertain.
Inputs: The minimum context + any data that must be used.
Examples: 1–3 examples when format or style matters.
Verification: A short rubric / checklist to catch common failures.
Iteration: Ask for clarifying questions or alternatives when confidence is low.

1) Define Success Criteria (Stop Asking for “Good”)

Most “bad model outputs” are actually bad acceptance criteria.

Better prompt pattern

Goal: {{one sentence}}
Success criteria:
- {{must-have 1}}
- {{must-have 2}}
- {{must-not-do}}

Examples of strong criteria:

“Includes only facts present in the provided doc.”
“Returns valid JSON matching the schema.”
“Lists tradeoffs + risks + next steps.”

2) Use an Output Contract (Force Structure)

If you care about reliability, define the output like a spec.

Output Contract Template (Copy-Paste)

Output format:
- Section 1: {{name}} ({{constraints}})
- Section 2: {{name}} ({{constraints}})
- Section 3: {{name}} ({{constraints}})

Style:
- {{tone}}
- {{reading level}}

Length:
- {{max words / bullets / lines}}

When you need machine-readability, use JSON:

{
    "summary": "2-3 sentences",
    "key_points": ["..."],
    "assumptions": ["..."],
    "risks": ["..."],
    "next_steps": ["..."]
}

3) Separate Instructions From Inputs (The “4-Block” Layout)

Mixing context + instructions in one blob makes prompts harder to follow and harder to debug.

4-Block Prompt Template

## INSTRUCTIONS
{{what to do}}

## INPUTS
{{the data, doc, or context}}

## CONSTRAINTS
{{scope, exclusions, uncertainty rule}}

## OUTPUT FORMAT
{{the contract / schema}}

4) Prefer Examples Over Adjectives (When Format Matters)

“Be concise and professional” is vague. An example output isn’t.

Mini few-shot pattern

Example output:
- {{bullet 1}}
- {{bullet 2}}
- {{bullet 3}}

Use examples when you see any of these failure modes:

The model drifts in formatting (headings/bullets/JSON shape)
It over-explains (wordy answers) or under-explains (missing steps)
You need consistent style across a team

5) Add a Lightweight Evaluator (Catch Mistakes Before You Ship)

Don’t rely on “looks good”. Add a tiny self-check the model must pass.

Self-check block (Copy-Paste)

Before finalizing, verify:
☐ Output matches the requested format exactly
☐ All success criteria are satisfied (list any misses)
☐ Claims not supported by inputs are marked as [UNCERTAIN]
☐ Next steps are specific and actionable (no vague advice)

Quick rubric (0–5)

Score the draft (0–5 each):
- Correctness
- Completeness
- Clarity
- Actionability

If any score < 4, revise once and rescore.

6) Model-Specific Notes (ChatGPT, Claude, Gemini)

These best practices work across models; the difference is what each model responds to best.

ChatGPT: do best with explicit formatting + constraints; great for code and structured outputs. Try the ChatGPT Prompt Generator.
Claude: excels with “contract-style” instructions and critique/evaluation steps. See Claude Prompt Generator and Claude Prompt Engineering Best Practices (2026).
Gemini: benefits from clear input labeling (especially multimodal) and explicit verification steps. Use Gemini Prompt Generator.

Copy-Paste Templates (Ready to Use)

A) “Best Practices Prompt Builder” (Generate a Better Prompt)

Use this inside Prompt Builder to generate a production-grade prompt:

Act as a senior prompt engineer.

Task: {{describe the task}}
User: {{who will run this prompt}}
Constraints: {{hard rules + exclusions}}
Output needed: {{format + length + tone}}

Create:
1) A system prompt (contract-style: role, goal, constraints, uncertainty rule, output format)
2) A user prompt using the 4-block layout (INSTRUCTIONS, INPUTS, CONSTRAINTS, OUTPUT FORMAT)
3) A short self-check evaluator (4 bullets max)

B) “Rewrite With Constraints” (Turn Messy Notes Into a Spec)

Rewrite the request below into a clear prompt spec:
- success criteria (3 bullets)
- constraints (3 bullets)
- output contract (sections + length)

Request:
{{paste messy request}}

C) “Two Options + Recommendation”

Generate two options.
For each: pros, cons, and risks.
Then recommend one option and explain why in 3 bullets.

Anti-Patterns That Still Break Prompts in 2026

Keyword dumping: long lists of instructions without priorities
Hidden constraints: expecting length/format/scope without stating it
Context dumping: large inputs with no “what matters most” guidance
One-shot shipping: no self-check, no rubric, no iteration
Conflicting goals: asking for “short” and “comprehensive” at the same time

FAQ

What’s the #1 prompt engineering best practice in 2026? Write success criteria and an output contract. Most failures are undefined “done.”

Do I need long prompts? No. You need structured prompts. Structure beats length.

How do I make prompts reliable for teams? Use templates + examples + a tiny evaluator. Then iterate using a small eval set.

Prompt Engineering Best Practices (2026): Updated Checklist + Templates