Prompt Engineering Plus

Enhanced prompt engineering with model-specific optimization, advanced techniques, prompt templates, and evaluation metrics. Covers LLMs, image generators, video models with chain-of-thought, few-shot, system prompts, and negative prompts.

Enhanced prompt engineering with model-specific optimization, advanced techniques, and evaluation metrics.

Features

• Model-Specific Tips: Optimized prompts for Claude, GPT-4, Gemini, FLUX, Veo
• Advanced Techniques: Chain-of-thought, few-shot, system prompts
• Prompt Templates: Ready-to-use templates for common tasks
• Evaluation Metrics: Measure prompt effectiveness
• Optimization Workflow: Iterative improvement process

Quick Reference

Task	Technique	Template
Code review	Role + constraints	Review this [language] code for...
Content writing	Audience + tone	Write a [type] about [topic] for [audience]
Image generation	Subject + style + composition	[Subject], [style], [lighting], [quality]
Data extraction	Schema + examples	Extract fields matching this schema...
Analysis	Step-by-step	Analyze [data] by [method]

LLM Prompting

Basic Structure

text

[Role/Context] + [Task] + [Constraints] + [Output Format]

Role Prompting

python

# Expert role
"You are a senior software engineer with 15 years of experience in [domain]."

# Teaching role
"You are a patient teacher explaining [topic] to a beginner."

# Critic role
"You are a constructive critic reviewing [work] with specific feedback."

Chain-of-Thought

python

# Explicit steps
"Think through this step by step before giving the final answer."

# Structured reasoning
"1. First, identify the key facts...
2. Then, analyze the relationships...
3. Finally, draw conclusions..."

Few-Shot Examples

python

# Example format
"Convert these sentences to formal business English:

Example 1:
Input: gonna send u the report tmrw
Output: I will send you the report tomorrow.

Example 2:
Input: cant make the meeting, something came up
Output: I apologize, but I will be unable to attend the meeting due to an unforeseen circumstance.

Now convert:
Input: hey can we push the deadline back a bit?"

Output Format Specification

python

# JSON schema
"Return a JSON array with objects containing 'text', 'sentiment' (positive/negative/neutral), and 'confidence' (0-1)."

# Structured output
"For each issue found, provide:
- Line number
- Issue description
- Severity (high/medium/low)
- Suggested fix"

Constraint Setting

python

# Word limits
"Summarize in exactly 3 bullet points, each under 20 words."

# Format constraints
"Return only valid JSON, no explanation."

# Content constraints
"Focus only on actionable insights, not background information."

Image Generation Prompting

Basic Structure

text

[Subject] + [Style] + [Composition] + [Lighting] + [Technical]

Style Keywords

python

# Photography
"shot on Kodak Portra 400 film, soft natural lighting, shallow depth of field"

# Digital art
"digital painting, concept art, artstation, trending"

# Oil painting
"oil painting, masterpiece, classical, detailed brushwork"

# 3D render
"3D render, octane render, cinema 4D, detailed"

Quality Keywords

python

# High quality
"photorealistic, 8K, ultra detailed, sharp focus, professional, masterpiece"

# Artistic
"intricate details, award-winning, best quality"

# Technical
"high resolution, 4K, detailed texture, sharp"

Negative Prompts

python

# Common negatives
"blurry, distorted, extra limbs, watermark, text, low quality, cartoon, anime"

# Photography negatives
"overexposed, underexposed, grainy, noisy, artifacts"

# Art negatives
"ugly, deformed, disfigured, poor proportions, bad anatomy"

Video Prompting

Basic Structure

text

[Shot Type] + [Subject] + [Action] + [Setting] + [Style]

Camera Movement

python

# Tracking
"Slow tracking shot following [subject] through [setting]"

# Pan
"Smooth pan across [scene] revealing [subject]"

# Zoom
"Slow zoom into [subject] with [effect]"

# Handheld
"Handheld camera following [subject] with natural movement"

Temporal Keywords

python

# Speed
"slow motion, timelapse, real-time, smooth motion"

# Duration
"continuous shot, quick cuts, frozen moment"

# Transitions
"dissolve, fade, wipe, morph"

Model-Specific Tips

Claude

• Excels at nuanced instructions
• Responds well to role-playing
• Good at following complex constraints
• Prefers explicit output formats

GPT-4

• Strong at code generation
• Works well with examples
• Good structured output
• Responds to "let's think step by step"

Gemini

• Good at multi-modal tasks
• Works well with visual prompts
• Strong at analysis tasks
• Prefers clear instructions

FLUX

• Detailed subject descriptions
• Style references work well
• Lighting keywords important
• Negative prompts supported

Veo

• Camera movement keywords
• Cinematic language works well
• Action descriptions important
• Include temporal context

Evaluation Metrics

Effectiveness Score

Metric	Description	Measurement
Clarity	How clear is the prompt?	1-5 rating
Specificity	How specific are the instructions?	Word count, detail level
Consistency	How consistent are results?	Variance across runs
Quality	How good is the output?	Manual review

Prompt Quality Checklist

• Clear objective stated
• Constraints defined
• Output format specified
• Examples provided (if needed)
• No conflicting instructions
• Appropriate length

Optimization Workflow

1. Start Simple

python

# Basic prompt
"Write a summary of this article."

2. Add Specificity

python

# More specific
"Write a 3-sentence summary of this article, focusing on key findings."

3. Add Constraints

python

# With constraints
"Write a 3-sentence summary of this article, focusing on key findings. Use formal business English. Include specific statistics if mentioned."

4. Add Examples

python

# With examples
"Write a 3-sentence summary of this article, focusing on key findings. Use formal business English. Include specific statistics if mentioned.

Example:
Input: 'Study finds 85% of users prefer interface X over Y.'
Output: 'A recent study reveals that 85% of users demonstrate a preference for Interface X compared to Interface Y. The findings suggest significant usability advantages for Interface X. This data should inform future interface design decisions.'"

5. Iterate Based on Results

python

# Refined prompt
"Write a 3-sentence summary of this article, focusing on key findings. Use formal business English. Include specific statistics if mentioned. Avoid technical jargon. Start with the most important finding.

Example:
Input: 'Study finds 85% of users prefer interface X over Y.'
Output: 'A recent study reveals that 85% of users demonstrate a preference for Interface X compared to Interface Y. The findings suggest significant usability advantages for Interface X. This data should inform future interface design decisions.'"

Common Mistakes

Mistake	Problem	Fix
Too vague	Unpredictable output	Add specifics
Too long	Model loses focus	Prioritize key info
Conflicting	Confuses model	Remove contradictions
No format	Inconsistent output	Specify format
No examples	Unclear expectations	Add few-shot

Prompt Templates

Code Review

python

"Review this [language] code for:
1. Bugs and logic errors
2. Security vulnerabilities
3. Performance issues
4. Code style/best practices

Code:
[code]

For each issue found, provide:
- Line number
- Issue description
- Severity (high/medium/low)
- Suggested fix"

Content Writing

python

"Write a [content type] about [topic].

Audience: [target audience]
Tone: [formal/casual/professional]
Length: [word count]
Key points to cover:
1. [point 1]
2. [point 2]
3. [point 3]

Include: [specific elements]
Avoid: [things to exclude]"

Data Analysis

python

"Analyze this data:
[data]

Provide:
1. Key insights (3-5 findings)
2. Patterns or trends
3. Anomalies or outliers
4. Recommendations based on findings

Use statistical terms where appropriate. Include confidence levels for key claims."

Image Generation

python

"[Subject with details], [setting/background], [lighting type],
[art style or photography style], [composition], [quality keywords]"

Best Practices

1. Start simple - Begin with basic prompts, add complexity as needed
2. Be specific - Vague prompts produce vague results
3. Use examples - Few-shot learning improves consistency
4. Set constraints - Word limits, formats, tone
5. Iterate - Refine based on results
6. Evaluate - Measure effectiveness
7. Document - Save successful prompts