elevator should leverage concepts from, or even fulltext of, state of the art prompt engineering guides

[phrazzld]

22365_3_Prompt Engineering_v7 (1).pdf

this and followup links: https://docs.anthropic.com/en/docs/build-with-claude/prompt-engineering/overview https://platform.openai.com/docs/guides/text?api-mode=responses https://www.promptingguide.ai/

[phrazzld]

Analysis: Integrating State-of-the-Art Prompt Engineering into Elevator

After analyzing the attached PDF and the linked prompt engineering resources from Anthropic, OpenAI, and PromptingGuide.ai, along with the current elevator implementation, I've identified several opportunities to significantly enhance the prompt transformation capabilities.

Current State

The elevator currently uses a basic elevation prompt (lines 38-48 in api.ts) that transforms requests into \"sophisticated, technically precise articulations.\" While functional, it doesn't leverage many of the advanced techniques from modern prompt engineering.

Key Findings from Prompt Engineering Resources

1. Structured Prompt Patterns

Modern prompt engineering emphasizes structured approaches like:

• CRISP Pattern: Context, Role, Instructions, Specifics, Parameters
• Chain-of-Thought: Step-by-step reasoning for complex tasks
• Few-Shot Learning: Providing examples to guide transformations

2. Context Enrichment

State-of-the-art techniques focus on:

• Adding domain-specific context
• Specifying constraints and format requirements
• Including success criteria and validation steps

3. Ambiguity Resolution

Advanced approaches handle vague requests by:

• Detecting unclear terms and expanding them
• Providing multiple interpretations
• Adding clarifying context

Proposed Architectural Approaches

Approach 1: Enhanced Single-Pass Transformation

Pros:

• Minimal changes to current architecture
• Fast execution
• Simple to implement

Cons:

• Limited ability to handle complex transformations
• May miss nuanced improvements

Implementation:

const ENHANCED_ELEVATION_PROMPT = `
<role>You are an expert prompt engineer specializing in transforming vague requests into precise, actionable prompts.</role>

<instructions>
Transform the user's request using these techniques:
1. Add specific context and constraints
2. Clarify ambiguous terms
3. Structure the request with clear sections
4. Include format specifications
5. Add success criteria
</instructions>

<examples>
Input: "Write about AI"
Output: "Create a 1000-word article about artificial intelligence applications in healthcare. Structure: introduction (200 words), three main applications with real-world examples (600 words total), future implications (200 words). Target audience: healthcare professionals. Tone: informative but accessible."
</examples>

<output>Provide only the enhanced prompt, no explanations.</output>
`;

Approach 2: Multi-Stage Pipeline Architecture

Pros:

• Sophisticated transformations
• Modular and extensible
• Can handle complex requests

Cons:

• Higher latency
• More API calls
• Increased complexity

Implementation:

interface PromptPipeline {
  analyze(prompt: string): PromptAnalysis;
  enrich(analysis: PromptAnalysis): EnrichedPrompt;
  structure(enriched: EnrichedPrompt): StructuredPrompt;
  optimize(structured: StructuredPrompt): string;
}

Approach 3: Template-Based Enhancement

Pros:

• Predictable outputs
• Domain-specific optimizations
• Fast execution

Cons:

• Less flexible
• Requires template maintenance
• May not handle edge cases well

Implementation:

const detectDomain = (prompt: string): Domain => { /* ... */ };
const applyTemplate = (prompt: string, domain: Domain): string => {
  const template = DOMAIN_TEMPLATES[domain];
  return template.transform(prompt);
};

Approach 4: Hybrid Intelligence System (Recommended)

Pros:

• Best of both worlds
• Adaptive to complexity
• Scalable

Cons:

• Most complex implementation
• Requires careful orchestration

Implementation:

class HybridPromptEnhancer {
  async enhance(prompt: string): Promise<string> {
    const complexity = this.assessComplexity(prompt);

    if (complexity === 'simple') {
      return this.templateEnhance(prompt);
    } else if (complexity === 'moderate') {
      return this.singlePassEnhance(prompt);
    } else {
      return this.pipelineEnhance(prompt);
    }
  }
}

Specific Enhancement Techniques to Implement

1. Prompt Analysis Module

interface PromptAnalysis {
  domain: string;
  intent: string;
  ambiguities: string[];
  missingContext: string[];
  suggestedStructure: string;
}

2. Context Injection

• Detect domain from keywords
• Add relevant technical context
• Include industry best practices

3. Structure Templates

const STRUCTURE_TEMPLATES = {
  technical: "Context: {}\nObjective: {}\nConstraints: {}\nDeliverables: {}",
  creative: "Theme: {}\nTone: {}\nAudience: {}\nFormat: {}",
  analytical: "Data: {}\nMethodology: {}\nMetrics: {}\nOutput: {}"
};

4. Few-Shot Example Bank

Build a repository of before/after transformations categorized by:

• Domain (tech, creative, business, academic)
• Task type (analysis, generation, transformation)
• Complexity level

Implementation Recommendation

I recommend Approach 4: Hybrid Intelligence System with phased implementation:

Phase 1: Enhanced Prompt (Quick Win)

Update the current ELEVATION_PROMPT with structured patterns and examples. This can be done immediately with minimal code changes.

Phase 2: Domain Detection

Add simple keyword-based domain detection to apply domain-specific enhancements.

Phase 3: Template System

Implement structured templates for common request types.

Phase 4: Full Pipeline

Build the complete multi-stage pipeline for complex transformations.

Specific Code Changes for Phase 1

// In api.ts, replace ELEVATION_PROMPT with:
const ELEVATION_PROMPT = `<system>
You are an expert prompt engineer. Transform vague requests into precise, actionable prompts using state-of-the-art techniques.
</system>

<techniques>
1. STRUCTURE: Use clear sections (Context, Task, Constraints, Output)
2. SPECIFICITY: Replace vague terms with precise technical language
3. EXAMPLES: Add relevant examples when helpful
4. CONSTRAINTS: Include format, length, tone, audience specifications
5. VALIDATION: Add success criteria and quality checks
</techniques>

<examples>
<example>
Input: "Help me with my code"
Output: "Review this TypeScript codebase for potential performance bottlenecks. Focus on: 1) Inefficient algorithms (O(n²) or worse), 2) Memory leaks in event handlers, 3) Unnecessary re-renders in React components. Provide specific line numbers and suggested optimizations for each issue found. Format: Markdown with code snippets."
</example>

<example>
Input: "Write about climate change"
Output: "Create a 1500-word research summary on climate change mitigation technologies. Structure: Executive summary (200 words), Current technologies overview (500 words) covering solar, wind, and carbon capture, Emerging technologies (500 words) including fusion and geoengineering, Policy recommendations (300 words). Target audience: Policymakers with limited technical background. Include 5+ peer-reviewed citations."
</example>
</examples>

<task>
Transform the following request into a sophisticated, precise prompt. Output ONLY the enhanced prompt, no explanations.
</task>`;

Metrics for Success

1. Quality Metrics

   • Ambiguity reduction score
   • Specificity increase
   • Structure clarity

2. User Metrics

   • API success rate improvement
   • User satisfaction scores
   • Time saved in prompt iteration

3. Technical Metrics

   • Latency (keep under 2s for 90% of requests)
   • Token efficiency
   • Error rate reduction

Next Steps

1. Immediate: Update ELEVATION_PROMPT with enhanced version (Phase 1)
2. Short-term: Implement domain detection and basic templates
3. Medium-term: Build example bank and template system
4. Long-term: Full pipeline architecture with adaptive complexity handling

This approach leverages the best practices from modern prompt engineering while maintaining the simplicity and effectiveness that makes elevator valuable.

[phrazzld]

My Recommendation: Start with Enhanced Prompt, Build Towards Hybrid System

After careful analysis, here's what I believe is the best path forward:

Immediate Action (Today)

Replace the current basic prompt with an enhanced version that incorporates proven prompt engineering patterns. This requires minimal code changes but delivers immediate value:

const ELEVATION_PROMPT = `<role>Expert prompt engineer specializing in precise technical communication</role>

<objective>Transform the user's request using these proven techniques:
- Add specific context and measurable outcomes
- Replace vague terms with precise technical language  
- Structure with clear sections: Context → Task → Constraints → Output
- Include relevant examples when patterns are detected
- Specify format, length, tone, and audience
</objective>

<patterns>
When you detect these patterns, apply specific enhancements:
- "help with X" → Add specific problems, success criteria, constraints
- "write about X" → Add word count, structure, audience, key points
- "analyze X" → Add methodology, metrics, output format, scope
- "create X" → Add specifications, requirements, examples, validation
</patterns>

<output_rules>
- Output ONLY the transformed prompt
- No explanations, headers, or meta-commentary
- Preserve user's core intent while maximizing clarity
- Make implicit requirements explicit
</output_rules>

Transform this request:`;

Short-Term Enhancement (Next 2 Weeks)

Add a lightweight pattern detection system:

// New file: src/patterns.ts
export const PROMPT_PATTERNS = {
  analysis: {
    keywords: ['analyze', 'review', 'evaluate', 'assess'],
    template: 'Analyze {subject} using {methodology}. Focus on: {aspects}. Deliverables: {outputs}. Constraints: {limits}.'
  },
  creation: {
    keywords: ['create', 'build', 'write', 'design'],
    template: 'Create {artifact} for {audience}. Requirements: {specs}. Format: {structure}. Success criteria: {validation}.'
  },
  // ... more patterns
};

// Enhanced api.ts
function detectPattern(prompt: string): PromptPattern | null {
  // Simple keyword matching initially
  // Can evolve to ML-based classification later
}

Medium-Term Goal (1-2 Months)

Build the example bank system:

// New file: src/examples.ts
export interface PromptExample {
  domain: string;
  before: string;
  after: string;
  techniques: string[];
}

export const EXAMPLE_BANK: PromptExample[] = [
  {
    domain: 'software',
    before: 'Fix my bug',
    after: 'Debug TypeError in React component OrderForm (line 45). Error occurs when user.address is undefined. Need to: 1) Add null checking, 2) Provide fallback values, 3) Update TypeScript types. Include unit tests for edge cases.',
    techniques: ['specificity', 'structure', 'validation']
  },
  // ... extensive example library
];

Long-Term Vision (3-6 Months)

Evolve into the hybrid system that adapts based on prompt complexity:

class AdaptivePromptEnhancer {
  async enhance(prompt: string): Promise<string> {
    const features = this.extractFeatures(prompt);

    // Simple prompts: Use templates (fast)
    if (features.wordCount < 10 && features.hasPattern) {
      return this.templateEnhance(prompt, features.pattern);
    }

    // Moderate complexity: Single LLM pass
    if (features.complexity === 'moderate') {
      return this.llmEnhance(prompt, this.selectExamples(features));
    }

    // Complex prompts: Multi-stage pipeline
    return this.pipelineEnhance(prompt, features);
  }
}

Why This Approach Works

1. Immediate Value: Enhanced prompt delivers better results today
2. Incremental Improvement: Each phase builds on the previous
3. User-Centric: Maintains fast response times while improving quality
4. Data-Driven: Can collect examples from actual usage
5. Future-Proof: Architecture allows for ML model integration later

The Key Insight

The best prompt engineering isn't about making prompts longer or more complex—it's about making them precise and actionable. Every enhancement should:

• Reduce ambiguity
• Add essential context
• Specify clear outputs
• Enable better results

This philosophy aligns perfectly with elevator's mission of transforming simple requests into sophisticated articulations.

Call to Action

1. Today: Update ELEVATION_PROMPT with the enhanced version
2. This Week: Add basic pattern detection
3. Next Sprint: Begin collecting real-world examples
4. Q1 2025: Launch adaptive enhancement system

The elevator project has the potential to become the standard tool for prompt enhancement. By incorporating these state-of-the-art techniques, we can help users get dramatically better results from AI systems while maintaining the simplicity that makes elevator special.