version: "1.0.0" name: ai-features description: "Use this skill when the user needs to add AI-powered features to their SaaS product, integrate LLM APIs, build AI assistants, implement RAG, or use AI to differentiate their product. Covers API selection, prompt engineering for product features, cost management, and building AI features that non-technical founders can ship and maintain."

AI Features & LLM Integration

AI features should make your product 10x better at its core job, not be a marketing checkbox. This skill helps you choose the right AI pattern, manage costs, and ship AI features that users actually value.

Core Principles

• AI features should make your product 10x better at its core job, not be a marketing checkbox.
• Start with the API, not a custom model. You don't need to train anything.
• Cost per API call matters at scale. Design for it from day one.
• Prompt engineering is your product differentiator. The model is the same for everyone.
• Always have a fallback. AI features should degrade gracefully, not crash the app.

When to Add AI Features

Add AI When:

• Users do something repetitive that AI could automate (drafting, categorizing, summarizing)
• Users need help interpreting data (analysis, recommendations, insights)
• You can save users significant time on a task they do frequently
• AI makes your product dramatically easier for non-experts to use
• Competitors have AI features and users expect parity

Don't Add AI When:

• It's just a chatbot wrapper with no product context
• You're adding it for marketing ("AI-powered!") without clear user benefit
• The task requires 100% accuracy (legal, medical, financial decisions)
• A simple rule-based approach would work just as well
• You haven't validated that users want it

AI Feature Patterns for SaaS

Pattern 1: Smart Drafts / Generation

What: AI writes a first draft that users edit and refine.

Examples: Email drafts, report summaries, product descriptions, social posts.

Tell AI:

Add an AI draft feature to [describe where in the app].
When the user clicks "Generate draft," call the Claude API with:
- Context from [what data the AI should use]
- A system prompt that produces [describe the output format]
- User can edit the result before saving
Include: loading state, error handling, and a "regenerate" button.
Use the Claude API with the claude-sonnet-4-5-20250929 model.

Pattern 2: Summarization / Analysis

What: AI condenses or interprets data the user has collected.

Examples: Meeting notes summary, customer feedback themes, dashboard insights.

Tell AI:

Add an AI summary feature that analyzes [data type].
Input: [describe the data — e.g., "all customer feedback from the last 30 days"]
Output: [describe what you want — e.g., "top 5 themes with supporting quotes"]
Display the summary in a card on [page name].
Cache the result so we don't re-call the API on every page load.

Pattern 3: Categorization / Tagging

What: AI automatically labels or categorizes incoming data.

Examples: Support ticket routing, lead scoring, content tagging.

Tell AI:

Auto-categorize incoming [items] using AI.
Categories: [list your categories]
When a new [item] is created, call the API to assign a category.
Store the result in the database. Allow users to override.
Use the cheapest model that works (start with claude-haiku-4-5-20251001).

Pattern 4: Smart Search / Q&A (RAG)

What: Users ask questions and get answers based on their own data.

Examples: "Search my documents," knowledge base Q&A, internal wiki search.

How RAG works (simplified):

1. User's documents → Split into chunks → Store as embeddings in vector DB
2. User asks a question → Convert to embedding → Find relevant chunks
3. Send relevant chunks + question to LLM → Get answer

Tell AI:

Add a Q&A feature where users can ask questions about their [data].
Use RAG (Retrieval-Augmented Generation):
- Embed their [documents/data] using [embedding model]
- Store embeddings in [Supabase pgvector / Pinecone]
- On query, retrieve top 5 relevant chunks
- Send to Claude with context for answer generation
Include: source citations, "I don't know" handling, loading state.

Pattern 5: AI-Powered Recommendations

What: Suggest next actions or choices based on user behavior and data.

Examples: "Try this feature next," product recommendations, workflow suggestions.

Choosing a Model

Rule of thumb: Start with the cheapest model. Only upgrade if quality isn't good enough.

Cost Management

Estimating Costs

Cost per request = (input tokens × input price) + (output tokens × output price)
 
Example (Claude Sonnet):
- Input: ~1,000 tokens ($0.003)
- Output: ~500 tokens ($0.0075)
- Cost per request: ~$0.01
 
1,000 requests/day = ~$10/day = ~$300/month

Reducing Costs

Setting Usage Limits

Free plan: 10 AI requests/day
Starter plan: 50 AI requests/day
Pro plan: 500 AI requests/day
Enterprise: Unlimited (with fair use policy)

Prompt Engineering for Product Features

Your prompts are your competitive advantage. Write them like product specs:

System Prompt Template

You are [role] helping [user type] with [task].
 
Context about this user:
- [User's plan/tier]
- [Relevant user data]
 
Rules:
- [Output format requirements]
- [Tone and style]
- [What NOT to include]
- [Length constraints]
 
Output format:
[Exact format you want]

Tips

• Be specific about output format — JSON, markdown, bullet points
• Include examples of good output in the prompt
• Set boundaries: what the AI should NOT do
• Test with edge cases: empty input, very long input, foreign languages
• Version your prompts and track which version performs best

Implementation Checklist

Before shipping an AI feature:
- [ ] Clear user value defined (what does this save/improve?)
- [ ] Model selected (cheapest that meets quality bar)
- [ ] System prompt written and tested with 10+ examples
- [ ] Loading state shown while AI processes
- [ ] Error handling: API down, rate limited, bad response
- [ ] Fallback if AI is unavailable (manual mode still works)
- [ ] Usage limits per plan tier
- [ ] Cost monitoring set up (track spend per day/week)
- [ ] User can edit/override AI output (AI assists, user decides)
- [ ] Response cached where appropriate

Common Mistakes

Success Looks Like

• AI features that users specifically mention as why they chose your product
• Cost per AI request tracked and predictable
• AI usage driving upgrades to higher tiers
• Users editing AI output 20-30% of the time (means AI is good but not blindly trusted)
• API costs are less than 10% of the revenue those features generate

Related Skills

• build — Hand your AI feature spec to Claude Code or Lovable and build it
• pricing — Design tiers with AI usage limits as a value metric
• analytics — Track AI feature usage and its impact on activation/retention
• secure — Protect user data flowing through AI APIs