Build Production-Ready AI Products with Proven Patterns
Master AI product development with RAG and LLM patterns.
Reduce latency from 30 seconds to milliseconds and build production-ready apps. Get the skill now.
LLM Ops
RAG
Prompt Engineering
AI UX
Vector Databases
About This Skill
This skill provides 5 core principles for AI product development, covering everything from RAG architecture to handling 1-30 second latencies. Learn to treat prompts as code and optimize costs for scale.
Quick Start
1Define your LLM integration pattern with validation layers
2Set up RAG architecture for dynamic knowledge retrieval
3Implement streaming and latency optimizations for better UX
Example Command
antigravity install ai-product
Core Capabilities
Probabilistic Design
Design for variance with validation layers and schema checks to handle non-deterministic LLM outputs effectively.
Prompt Engineering
Treat prompts as version-controlled code with regression testing and A/B testing frameworks to ensure reliability.
RAG over Fine-Tuning
Implement Retrieval-Augmented Generation over fine-tuning for faster updates and lower maintenance costs.
Design for Latency
Manage 1-30 second response times through streaming, caching, and aggressive pre-computation strategies.
Cost Optimization
Treat API costs as a core feature by optimizing token usage and selecting the right models for specific tasks.
Usage Examples
Before
Directly parsing LLM strings into SQL queries which leads to injection risks and broken schemas.
After
Schema validation with fallback to human review for edge cases, ensuring data integrity.
Input
Should I fine-tune GPT-4 for my company's internal documentation?
Output
No, start with RAG. Use a vector store to retrieve relevant docs at query time to avoid stale data and high training costs.
Input
Users are complaining about the 10-second wait for AI responses.
Output
Enable streaming responses with a typing indicator and implement server-side caching for common queries.
SKILL.md
---
name: ai-product
description: Every product will be AI-powered. The question is whether you'll
build it right or ship a demo that falls apart in production.
risk: safe
source: vibeship-spawner-skills (Apache 2.0)
date_added: 2026-02-27
---
# AI Product Development
Every product will be AI-powered. The question is whether you'll build it
right or ship a demo that falls apart in production.
This skill covers LLM integration patterns, RAG architecture, prompt
engineering that scales, AI UX that users trust, and cost optimization
that doesn't bankrupt you.
## Principles
- LLMs are probabilistic, not deterministic | Description: The same input can give different outputs. Design for variance.
Add validation layers. Never trust output blindly. Build for the
edge cases that will definitely happen. | Examples: Good: Validate LLM output against schema, fallback to human review | Bad: Parse LLM response and use directly in database
- Prompt engineering is product engineering | Description: Prompts are code. Version them. Test them. A/B test them. Document them.
One word change can flip behavior. Treat them with the same rigor as code. | Examples: Good: Prompts in version control, regression tests, A/B testing | Bad: Prompts inline in code, changed ad-hoc, no testing
- RAG over fine-tuning for most use cases | Description: Fine-tuning is expensive, slow, and hard to update. RAG lets you add
knowledge without retraining. Start with RAG. Fine-tune only when RAG
hits clear limits. | Examples: Good: Company docs in vector store, retrieved at query time | Bad: Fine-tuned model on company data, stale after 3 months
- Design for latency | Description: LLM calls take 1-30 seconds. Users hate waiting. Stream responses.
Show progress. Pre-compute when possible. Cache aggressively. | Examples: Good: Streaming response with typing indicator, cached embeddings | Bad: Spinner for 15 seconds, then wall of text appears
- Cost is a feature | Description: LLM API costs add up fast. At
Frequently Asked Questions
FAQ
Which LLM tools are compatible with this skill?
This skill is model-agnostic and works with OpenAI, Anthropic, LangChain, and custom RAG stacks.
Who is the target audience for this skill?
It is designed for software developers, AI engineers, and product managers building production-grade AI applications.
How does this differ from basic prompt engineering guides?
It focuses on production concerns like latency, cost, and reliability rather than just writing better text prompts.
Does this skill support non-English language models?
Yes, the architectural principles like RAG and probabilistic design apply to any language supported by your chosen LLM.
What are the expected results after implementing these patterns?
You can expect higher reliability, lower API costs, and a significantly better user experience through reduced perceived latency.
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Trigger Phrases
Use these phrases to activate this skill in your AI coding assistant:
build AI productRAG vs fine-tuningoptimize LLM costhandle LLM latencyprompt versioning
