ai-product
Build Production-Ready AI Products
Transform AI demos into production systems. This skill teaches LLM integration patterns, RAG architecture, and validation strategies so you can ship AI features that work at scale.
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Pruébalo
Usando "ai-product". Help me design a customer support AI that validates all outputs before responding to users
Resultado esperado:
Here's a production-ready architecture for your customer support AI:
**Prompt Structure**: System prompt with strict output format instructions, user query in template with validation hooks
**Validation Layers**:
1. Schema validation - ensure response matches expected JSON structure
2. Safety check - scan for harmful content before responding
3. Factual verification - cross-reference claims with knowledge base
4. Format enforcement - validate response length and structure
**Error Handling**: Graceful degradation with fallback to human agent when confidence is low
Usando "ai-product". My LLM is too slow. How can I improve perceived latency?
Resultado esperado:
Implement streaming with these techniques:
1. **Server-Sent Events (SSE)**: Stream tokens as they're generated
2. **Progressive UI**: Show skeleton first, fill in content as it arrives
3. **Optimistic Updates**: Display likely response while AI generates
4. **Chunked Generation**: Break complex responses into smaller pieces
This can reduce perceived latency by 40-60% without faster model response times.
Auditoría de seguridad
SeguroStatic analysis detected 2 false positive patterns related to 'weak cryptographic algorithm' at lines 3 and 58. These are misidentified keywords ('patterns', 'architecture') in documentation text. This is a documentation-only skill with no executable code, network calls, scripts, filesystem access, or external commands. Safe for publication.
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Lo que puedes crear
Architecting LLM Applications
Design production systems that use LLMs safely and reliably with proper validation and error handling.
Validating AI Outputs
Implement safety systems and validation layers to catch hallucinations and harmful content before serving to users.
Optimizing AI Costs
Reduce LLM API costs by 80% through prompt optimization, context management, and efficient token usage.
Prueba estos prompts
I need to build an AI-powered feature that [describe use case]. Help me design a production-ready architecture including: 1) How to structure prompts for reliability 2) What validation layers I need 3) How to handle failures gracefully 4) Cost optimization strategies
Review this prompt for production deployment: [insert prompt]. Identify: 1) Potential failure modes 2) Missing validation steps 3) Context window optimization opportunities 4) Cost concerns
My AI system is producing hallucinations in [specific context]. The current prompt is [insert prompt]. Suggest modifications to reduce hallucinations while maintaining accuracy.
I need my LLM to return [describe desired output format]. Help me: 1) Design the schema 2) Write the prompt with proper instructions 3) Add validation logic 4) Handle parsing failures gracefully
Mejores prácticas
- Treat prompts as code: version control, test in CI/CD, and review changes through pull requests
- Always validate LLM outputs with schema validation and safety checks before using them
- Build defense in depth: multiple validation layers catch what single checks miss
Evitar
- Shipping AI demos without production hardening - users will encounter failures at scale
- Stuffing context windows with irrelevant data - increases costs and reduces accuracy
- Trusting LLM outputs without validation - hallucinations will reach production users