/ai-product

name: ai-product
description: "Use when building AI features into a product: LLM integration, RAG pipelines, guardrails, streaming, AI UX, prompt engineering, or AI cost control. Treats prompts as code and validates every model output."
domain: pm
supports: [claude-code, cursor, codex, generic]
version: 0.1.0

AI Product Development

Expert in shipping production-grade AI-powered features — LLM integration patterns, RAG architecture, prompt engineering that scales, AI UX that users trust, safety and guardrails, streaming, and cost optimization. Treats prompts as code, validates all outputs, and never trusts an LLM blindly.

**Use when**: building AI features into a product, integrating LLMs, designing RAG pipelines, implementing AI safety/guardrails, optimizing AI costs, building AI UX patterns, prompt engineering for production, handling hallucinations, streaming LLM responses, or evaluating AI output quality.

---

When This Skill Is Activated

1. Read this file fully before proceeding 2. Understand what AI feature the user is building 3. Apply the relevant patterns below (integration, RAG, UX, safety, cost) 4. Always address: output validation, error handling, cost awareness, user trust

---

Core Principle

Demos are easy. Production is hard. Every pattern below exists because something broke in production.

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LLM Integration Patterns

Structured Output with Validation

Never parse free-text LLM output with regex. Use structured output modes and validate with a schema.

import { z } from "zod";
import OpenAI from "openai";

// 1. Define your schema
const ProductReviewSchema = z.object({
  sentiment: z.enum(["positive", "negative", "neutral"]),
  score: z.number().min(0).max(10),
  summary: z.string().max(200),
  keyTopics: z.array(z.string()).max(5),
});

type ProductReview = z.infer<typeof ProductReviewSchema>;

// 2. Call with structured output
const openai = new OpenAI();

async function analyzeReview(reviewText: string): Promise<ProductReview> {
  const response = await openai.chat.completions.create({
    model: "gpt-4o-mini",
    response_format: { type: "json_object" },
    messages: [
      {
        role: "system",
        content: `Analyze the product review. Return JSON matching this schema:
          { sentiment: "positive"|"negative"|"neutral", score: 0-10, summary: string, keyTopics: string[] }`,
      },
      { role: "user", content: reviewText },
    ],
  });

  const raw = JSON.parse(response.choices[0].message.content!);

  // 3. Always validate — the model can return anything
  const result = ProductReviewSchema.parse(raw);
  return result;
}

Streaming with Progress

Stream LLM responses to reduce perceived latency. Show users something is happening immediately.

async function streamResponse(prompt: string, onChunk: (text: string) => void) {
  const stream = await openai.chat.completions.create({
    model: "gpt-4o-mini",
    messages: [{ role: "user", content: prompt }],
    stream: true,
  });

  let fullText = "";
  for await (const chunk of stream) {
    const delta = chunk.choices[0]?.delta?.content || "";
    fullText += delta;
    onChunk(delta); // Update UI incrementally
  }

  return fullText;
}

// React example: streaming into state
function useStreamingAI() {
  const [text, setText] = useState("");
  const [isStreaming, setIsStreaming] = useState(false);

  const generate = async (prompt: string) => {
    setIsStreaming(true);
    setText("");
    await streamResponse(prompt, (chunk) => {
      setText((prev) => prev + chunk);
    });
    setIsStreaming(false);
  };

  return { text, isStreaming, generate };
}

Prompt Versioning and Testing

Treat prompts as code. Version them. Test with regression suites.

// prompts/v3-review-analyzer.ts
export const REVIEW_ANALYZER_PROMPT = {
  version: "3.0",
  system: `You are a product review analyst. Extract sentiment, score, summary, and topics.
    Always return valid JSON. Never hallucinate topics not mentioned in the review.`,
  temperature: 0.1, // Low temp for consistent structured output
  maxTokens: 500,
};

// tests/prompts/review-analyzer.test.ts
describe("Review Analyzer Prompt v3", () => {
  const testCases = [
    {
      input: "This product is amazing! Great battery life and beautiful screen.",
      expected: { sentiment: "positive", minScore: 7 },
    },
    {
      input: "Terrible. Broke after 2 days. Worst purchase ever.",
      expected: { sentiment: "negative", maxScore: 3 },
    },
    {
      input: "It's okay. Does what it says but nothing special.",
      expected: { sentiment: "neutral", minScore: 4, maxScore: 6 },
    },
  ];

  test.each(testCases)("correctly analyzes: $input", async ({ input, expected }) => {
    const result = await analyzeReview(input);
    expect(result.sentiment).toBe(expected.sentiment);
    if (expected.minScore) expect(result.score).toBeGreaterThanOrEqual(expected.minScore);
    if (expected.maxScore) expect(result.score).toBeLessThanOrEqual(expected.maxScore);
  });
});

---

RAG Architecture

When to Use RAG

| Approach | When | Example | |----------|------|---------| | Prompt only | Model already knows the answer | General knowledge questions | | RAG | Answer depends on your data | "What's our refund policy?" | | Fine-tuning | Model needs new behavior/style | Domain-specific tone or format | | RAG + Fine-tuning | Both custom data and custom behavior | Enterprise support bot |

RAG Pipeline

User Query
    ↓
Query Processing (rewrite, expand, decompose)
    ↓
Embedding (text → vector)
    ↓
Vector Search (find relevant chunks)
    ↓
Re-ranking (order by relevance)
    ↓
Context Assembly (fit within token budget)
    ↓
LLM Generation (with retrieved context)
    ↓
Citation Extraction + Validation
    ↓
Response with Sources

Implementation

import { OpenAI } from "openai";

const openai = new OpenAI();

// 1. Chunk documents at inge