Building AI-Powered Apps: Essential Prompts for Modern Development

Building applications with AI integration requires a new set of skills. This guide provides the prompts you need at every stage of AI-powered app development.

Planning Phase Prompts

Use Case Validation

"I want to build [app description]. Analyze whether AI is the right solution, what AI capabilities I need, potential challenges, and alternative approaches. Consider cost, complexity, and user experience."

Architecture Planning

"Design the architecture for an AI-powered [app type] that needs to [requirements]. Include: component diagram, data flow, AI model selection, API design, and scaling considerations."

Cost Estimation

"Estimate the AI costs for an application with [user count] users making [requests/day] requests. Compare costs across OpenAI, Anthropic, and open-source alternatives. Include compute, storage, and API costs."

Development Phase Prompts

Prompt Engineering System

"Create a prompt management system for my app that includes: prompt versioning, A/B testing capability, performance monitoring, and easy updates without deployment. Use [technology stack]."

Error Handling

"Design error handling for AI API calls including: rate limiting, timeout handling, fallback responses, retry logic, and user-friendly error messages. Consider partial failures and degraded service modes."

Response Parsing

"Create a robust system to parse AI responses that handles: structured output extraction, validation, type conversion, and graceful handling of unexpected formats. Include examples and edge cases."

Integration Prompts

API Wrapper

"Create a TypeScript wrapper for [AI provider] API that includes: type definitions, automatic retries, rate limiting, response caching, error handling, and usage logging. Follow best practices for SDK design."

Streaming Responses

"Implement streaming AI responses in [framework] with: progressive UI updates, cancellation support, error recovery mid-stream, and proper cleanup. Include frontend and backend code."

Context Management

"Design a conversation context management system that handles: token counting, context truncation, conversation summarization, and relevant context retrieval. Optimize for both quality and cost."

Testing Prompts

AI Response Testing

"Create a testing strategy for AI-powered features including: deterministic test cases, fuzzy matching for AI outputs, regression testing for prompt changes, and performance benchmarks."

Prompt Evaluation

"Build an evaluation framework for my prompts that measures: response quality, consistency, latency, cost per query, and user satisfaction. Include automated scoring and human evaluation protocols."

Deployment Prompts

Monitoring Setup

"Design monitoring for AI features including: response quality metrics, latency tracking, cost alerts, error rates, and user feedback collection. Create dashboards and alerting rules."

A/B Testing

"Implement A/B testing for AI prompts that allows: gradual rollout, statistical significance calculation, automatic winner selection, and rollback capability. Include analytics integration."

Optimization Prompts

Cost Optimization

"Analyze my AI usage and suggest optimizations including: caching strategies, prompt compression, model selection by task, and batching opportunities. Target 50% cost reduction while maintaining quality."

Latency Optimization

"Optimize my AI feature for latency including: async processing, speculative execution, response streaming, and edge caching. Target sub-2-second response time."

Conclusion

Building AI-powered applications is a new discipline that combines traditional software engineering with prompt engineering and AI operations. These prompts will help you navigate each phase confidently and build applications that deliver real value.