AI Prompt Templating Engine
Template-based prompt building with brand consistency and security.
When to Use This Skill
- Managing AI prompts across a codebase
- Need brand consistency in generated content
- Preventing prompt injection attacks
- Optimizing token usage with compact context
Core Concepts
Prompt engineering challenges:
- Scattered prompts - Hard to maintain consistency
- Brand drift - Generated content doesn't match brand
- Injection attacks - User input can hijack prompts
- Token waste - Verbose context burns budget
Implementation
TypeScript
// Types
interface PromptTemplate {
name: string;
version: string;
basePrompt: string;
placeholders: string[];
qualityModifiers: string[];
}
interface BrandKitContext {
primaryColors: string[];
accentColors: string[];
headlineFont?: string;
bodyFont?: string;
tone?: string;
}
interface ResolvedBrandContext {
primaryColor?: string;
secondaryColor?: string;
accentColor?: string;
gradient?: string;
font?: string;
tone?: string;
intensity: 'subtle' | 'balanced' | 'strong';
}
// Security: Input Sanitization
const MAX_INPUT_LENGTH = 500;
const SANITIZE_PATTERN = /[<>{}\[\]\\|`~]/g;
const INJECTION_PATTERNS = [
/ignore\s+(previous|above|all)/i,
/disregard\s+(previous|above|all)/i,
/system\s*:/i,
/assistant\s*:/i,
/\[INST\]/i,
/<<SYS>>/i,
];
function sanitizeInput(input: string): string {
if (input.length > MAX_INPUT_LENGTH) {
input = input.slice(0, MAX_INPUT_LENGTH);
}
input = input.replace(SANITIZE_PATTERN, '');
for (const pattern of INJECTION_PATTERNS) {
if (pattern.test(input)) {
throw new Error('Potential prompt injection detected');
}
}
return input.trim();
}
function sanitizePlaceholders(placeholders: Record<string, string>): Record<string, string> {
const sanitized: Record<string, string> = {};
for (const [key, value] of Object.entries(placeholders)) {
sanitized[key] = sanitizeInput(value);
}
return sanitized;
}
// Brand Context Resolver
class BrandContextResolver {
resolve(
brandKit: BrandKitContext,
options: {
primaryColorIndex?: number;
secondaryColorIndex?: number;
accentColorIndex?: number;
useGradient?: boolean;
intensity?: 'subtle' | 'balanced' | 'strong';
} = {}
): ResolvedBrandContext {
const primaryColor = this.resolveColor(brandKit.primaryColors, options.primaryColorIndex ?? 0);
const secondaryColor = this.resolveColor(brandKit.primaryColors, options.secondaryColorIndex ?? 1);
const accentColor = this.resolveColor(brandKit.accentColors, options.accentColorIndex ?? 0);
const gradient = options.useGradient && primaryColor && secondaryColor
? `${primaryColor}→${secondaryColor}`
: undefined;
return {
primaryColor,
secondaryColor,
accentColor,
gradient,
font: brandKit.headlineFont,
tone: brandKit.tone,
intensity: options.intensity || 'balanced',
};
}
private resolveColor(colors: string[], index: number): string | undefined {
if (!colors.length) return undefined;
return colors[Math.min(index, colors.length - 1)];
}
}
// Compact brand block (~50-80 tokens)
function toCompactBrandBlock(ctx: ResolvedBrandContext): string {
const parts: string[] = [];
const colors = [ctx.primaryColor, ctx.secondaryColor, ctx.accentColor].filter(Boolean);
if (colors.length) parts.push(`Colors: ${colors.join(', ')}`);
if (ctx.gradient) parts.push(`Gradient: ${ctx.gradient}`);
if (ctx.font) parts.push(`Font: ${ctx.font}`);
if (ctx.tone) parts.push(`Tone: ${ctx.tone}`);
if (!parts.length) return '';
return `[BRAND: ${ctx.intensity} - ${parts.join(' | ')}]`;
}
// Template Loader with caching
const templateCache = new Map<string, PromptTemplate>();
async function loadTemplate(templateName: string): Promise<PromptTemplate> {
if (templateCache.has(templateName)) {
return templateCache.get(templateName)!;
}
// Prevent directory traversal
const normalized = templateName.replace(/\.\./g, '').replace(/[<>:"|?*]/g, '');
const content = await fs.readFile(`prompts/${normalized}.yaml`, 'utf-8');
const data = yaml.load(content) as any;
const template: PromptTemplate = {
name: data.name || templateName,
version: data.version || '1.0.0',
basePrompt: data.base_prompt,
placeholders: data.placeholders || [],
qualityModifiers: data.quality_modifiers || [],
};
// Validate placeholders exist in prompt
for (const placeholder of template.placeholders) {
if (!template.basePrompt.includes(`{${placeholder}}`)) {
throw new Error(`Placeholder {${placeholder}} not found in template`);
}
}
templateCache.set(templateName, template);
return template;
}
// Prompt Engine
const INTENSITY_MODIFIERS = {
subtle: 'subtly incorporate',
balanced: 'use',
strong: 'prominently feature',
};
class PromptEngine {
private brandResolver = new BrandContextResolver();
async buildPrompt(
templateName: string,
placeholders: Record<string, string>,
brandKit?: BrandKitContext,
brandOptions?: Parameters<BrandContextResolver['resolve']>[1]
): Promise<string> {
const sanitizedPlaceholders = sanitizePlaceholders(placeholders);
const template = await loadTemplate(templateName);
// Substitute placeholders
let prompt = template.basePrompt;
for (const [key, value] of Object.entries(sanitizedPlaceholders)) {
prompt = prompt.replace(new RegExp(`\\{${key}\\}`, 'g'), value);
}
// Inject brand context
if (brandKit) {
const resolved = this.brandResolver.resolve(brandKit, brandOptions);
const brandBlock = toCompactBrandBlock(resolved);
if (brandBlock) {
const modifier = INTENSITY_MODIFIERS[resolved.intensity];
prompt = `${prompt}\n\n${modifier} the following brand guidelines:\n${brandBlock}`;
}
}
// Add quality modifiers
if (template.qualityModifiers.length) {
prompt = `${prompt}\n\nQuality: ${template.qualityModifiers.join(', ')}`;
}
return prompt;
}
}
export const promptEngine = new PromptEngine();Python
import re
import yaml
from dataclasses import dataclass
from typing import Dict, List, Optional
from pathlib import Path
MAX_INPUT_LENGTH = 500
SANITIZE_PATTERN = re.compile(r'[<>{}\[\]\\|`~]')
INJECTION_PATTERNS = [
re.compile(r'ignore\s+(previous|above|all)', re.I),
re.compile(r'disregard\s+(previous|above|all)', re.I),
re.compile(r'system\s*:', re.I),
re.compile(r'assistant\s*:', re.I),
re.compile(r'\[INST\]', re.I),
]
def sanitize_input(input_str: str) -> str:
if len(input_str) > MAX_INPUT_LENGTH:
input_str = input_str[:MAX_INPUT_LENGTH]
input_str = SANITIZE_PATTERN.sub('', input_str)
for pattern in INJECTION_PATTERNS:
if pattern.search(input_str):
raise ValueError("Potential prompt injection detected")
return input_str.strip()
@dataclass
class PromptTemplate:
name: str
version: str
base_prompt: str
placeholders: List[str]
quality_modifiers: List[str]
@dataclass
class BrandKitContext:
primary_colors: List[str]
accent_colors: List[str]
headline_font: Optional[str] = None
tone: Optional[str] = None
@dataclass
class ResolvedBrandContext:
primary_color: Optional[str] = None
secondary_color: Optional[str] = None
accent_color: Optional[str] = None
gradient: Optional[str] = None
font: Optional[str] = None
tone: Optional[str] = None
intensity: str = "balanced"
class BrandContextResolver:
def resolve(
self,
brand_kit: BrandKitContext,
primary_index: int = 0,
secondary_index: int = 1,
accent_index: int = 0,
use_gradient: bool = False,
intensity: str = "balanced",
) -> ResolvedBrandContext:
primary = self._resolve_color(brand_kit.primary_colors, primary_index)
secondary = self._resolve_color(brand_kit.primary_colors, secondary_index)
accent = self._resolve_color(brand_kit.accent_colors, accent_index)
gradient = f"{primary}→{secondary}" if use_gradient and primary and secondary else None
return ResolvedBrandContext(
primary_color=primary,
secondary_color=secondary,
accent_color=accent,
gradient=gradient,
font=brand_kit.headline_font,
tone=brand_kit.tone,
intensity=intensity,
)
def _resolve_color(self, colors: List[str], index: int) -> Optional[str]:
if not colors:
return None
return colors[min(index, len(colors) - 1)]
def to_compact_brand_block(ctx: ResolvedBrandContext) -> str:
parts = []
colors = [c for c in [ctx.primary_color, ctx.secondary_color, ctx.accent_color] if c]
if colors:
parts.append(f"Colors: {', '.join(colors)}")
if ctx.gradient:
parts.append(f"Gradient: {ctx.gradient}")
if ctx.font:
parts.append(f"Font: {ctx.font}")
if ctx.tone:
parts.append(f"Tone: {ctx.tone}")
if not parts:
return ""
return f"[BRAND: {ctx.intensity} - {' | '.join(parts)}]"
_template_cache: Dict[str, PromptTemplate] = {}
def load_template(template_name: str) -> PromptTemplate:
if template_name in _template_cache:
return _template_cache[template_name]
# Prevent directory traversal
safe_name = template_name.replace("..", "").replace("/", "_")
path = Path("prompts") / f"{safe_name}.yaml"
with open(path) as f:
data = yaml.safe_load(f)
template = PromptTemplate(
name=data.get("name", template_name),
version=data.get("version", "1.0.0"),
base_prompt=data["base_prompt"],
placeholders=data.get("placeholders", []),
quality_modifiers=data.get("quality_modifiers", []),
)
_template_cache[template_name] = template
return template
INTENSITY_MODIFIERS = {
"subtle": "subtly incorporate",
"balanced": "use",
"strong": "prominently feature",
}
class PromptEngine:
def __init__(self):
self._brand_resolver = BrandContextResolver()
def build_prompt(
self,
template_name: str,
placeholders: Dict[str, str],
brand_kit: Optional[BrandKitContext] = None,
intensity: str = "balanced",
use_gradient: bool = False,
) -> str:
# Sanitize inputs
sanitized = {k: sanitize_input(v) for k, v in placeholders.items()}
template = load_template(template_name)
# Substitute placeholders
prompt = template.base_prompt
for key, value in sanitized.items():
prompt = prompt.replace(f"{{{key}}}", value)
# Inject brand context
if brand_kit:
resolved = self._brand_resolver.resolve(
brand_kit, intensity=intensity, use_gradient=use_gradient
)
brand_block = to_compact_brand_block(resolved)
if brand_block:
modifier = INTENSITY_MODIFIERS[resolved.intensity]
prompt = f"{prompt}\n\n{modifier} the following brand guidelines:\n{brand_block}"
# Add quality modifiers
if template.quality_modifiers:
prompt = f"{prompt}\n\nQuality: {', '.join(template.quality_modifiers)}"
return prompt
prompt_engine = PromptEngine()Template Example
# prompts/thumbnail_gaming.yaml
name: thumbnail_gaming
version: "1.0.0"
base_prompt: |
Create a {game_name} thumbnail.
Feature {subject} with {emotion} expression.
Style: {style}
placeholders:
- game_name
- subject
- emotion
- style
quality_modifiers:
- ultra detailed
- cinematic lighting
- 8K qualityUsage Examples
const prompt = await promptEngine.buildPrompt(
'thumbnail_gaming',
{
game_name: 'Cyberpunk 2077',
subject: 'character with katana',
emotion: 'intense',
style: 'neon cyberpunk',
},
{
primaryColors: ['#FF00FF', '#00FFFF'],
accentColors: ['#FFFF00'],
headlineFont: 'Orbitron',
tone: 'edgy',
},
{ useGradient: true, intensity: 'strong' }
);
// Result:
// Create a Cyberpunk 2077 thumbnail.
// Feature character with katana with intense expression.
// Style: neon cyberpunk
//
// prominently feature the following brand guidelines:
// [BRAND: strong - Colors: #FF00FF, #00FFFF, #FFFF00 | Gradient: #FF00FF→#00FFFF | Font: Orbitron | Tone: edgy]
//
// Quality: ultra detailed, cinematic lighting, 8K qualityBest Practices
- Sanitize all user inputs before substitution
- Use compact brand blocks to save tokens
- Cache templates for performance
- Validate placeholders exist in templates
- Use intensity modifiers for brand prominence
Common Mistakes
- No input sanitization (injection vulnerability)
- Verbose brand context (wastes tokens)
- Hardcoded prompts (inconsistent)
- Missing placeholder validation
- No template caching (slow)
Related Patterns
- ai-generation-client - Use prompts with AI APIs
- rate-limiting - Protect AI quota
- validation-quarantine - Validate AI outputs