| name | ai-expert |
| description | AI Expert - Complete artificial intelligence implementation for products, features, and automation. Use when: implementing AI features, machine learning, NLP, automation, chatbots, predictive models. Include: AI/ML implementation, LLM integration, prompt engineering, AI architecture, AI ethics. Works with: Claude Code, OpenCode, Cursor, Any SKILL.md agent.
|
| version | 1.0.0 |
| author | Business Brain |
| license | MIT |
| tags | ["ai","machine-learning","llm","nlp","automation","chatgpt"] |
| agents | ["universal"] |
AI Expert Skill
Complete AI implementation and integration for products and business.
AI Implementation Types
| Type | Use Cases | Complexity |
|---|
| Rule-based | Chatbots, routing | Low |
| ML/Statistical | Recommendations, predictions | Medium |
| LLM-based | Content generation, Q&A | Medium |
| Generative | Images, code, synthesis | High |
| Multimodal | Vision + language | Very High |
AI Architecture Patterns
Pattern 1: AI as Feature
User → App → AI Model → Response → App → User
Pattern 2: AI Agent
User → Agent → Tools + AI → Action → User
Pattern 3: RAG (Retrieval Augmented)
Query → Embed → Vector DB → Context → LLM → Response
LLM Integration
Available Models
| Provider | Model | Strength | Cost |
|---|
| OpenAI | GPT-4 | Reasoning | $$$ |
| OpenAI | GPT-4o | Multimodal | $$ |
| Anthropic | Claude | Long context | $$$ |
| Google | Gemini | Multimodal | $$ |
| Meta | Llama | Local | Free |
| Mistral | Mixtral | Open | $ |
| Cohere | Command | Enterprise | $$ |
API Integration Template
import openai
def generate_response(prompt, context=None):
response = openai.ChatCompletion.create(
model="gpt-4",
messages=[
{"role": "system", "content": system_prompt},
{"role": "user", "content": prompt}
],
temperature=0.7,
max_tokens=1000
)
return response.choices[0].message.content
Prompt Engineering
Prompt Framework
<instructions>
You are [role]
</instructions>
<context>
[Relevant background]
</context>
<task>
[Specific request]
</task>
<format>
[Output format]
</format>
Few-Shot Examples
Input: This is great!
Output: positive
Input: Not what I expected
Output: negative
Input: Customer: [user input]
Agent: [response]
Chain of Thought
Step 1: Understand the problem
Step 2: Break into components
Step 3: Solve each component
Step 4: Combine solutions
Step 5: Verify answer
ReAct (Reason + Act)
Thought: I need to find the current price.
Action: Search query for "stock price"
Observation: Price is $50
Thought: Now I can calculate...
RAG Implementation
Vector Database Options
| Database | Type | Best For |
|---|
| Pinecone | SaaS | Scale |
| Weaviate | Open source | Control |
| Chroma | Local | Prototyping |
| pgvector | Postgres extension | Existing DBs |
RAG Pipeline
1. Text Source
2. Chunk (500-1000 tokens)
3. Embed (OpenAI ada)
4. Store (Vector DB)
5. Query
6. Retrieve top-k
7. Inject context
8. Generate response
AI Product Features
Common Features
| Feature | AI Type | Implementation |
|---|
| Search | Semantic | Embed + similarity |
| Recommendations | ML | Collaborative filtering |
| Classification | ML | Text classification |
| Summarization | LLM | Prompt engineering |
| Q&A | RAG | Knowledge base |
| Chatbot | LLM | Conversation flow |
| Translation | API | Google/DeepL API |
| Speech-to-Text | API | Whisper API |
AI Ethics & Safety
Considerations
| Issue | Mitigation |
|---|
| Bias in data | Audit training data |
| Privacy | Anonymize, don't train |
| Hallucinations | Verify with sources |
| Copyright | Use licensed content |
| Accessibility | Test with ScreenReader |
Guidelines
Testing AI
Metrics
| Type | Metric |
|---|
| Generative | Coherence, relevance |
| Classification | Accuracy, F1 |
| Similarity | Precision @ k |
| RAG | Context relevance |
A/B Testing
prompts = [variant_a, variant_b, variant_c]
results = [test_model(p) for p in prompts]
print(choose_best(results))
Implementation Checklist
Cost Optimization
Strategies
| Strategy | Savings |
|---|
| Smaller models | 70-90% |
| Caching | 30-50% |
| Batching | 20-40% |
| Fine-tuned small | 40-60% |
Usage Examples
User: "Add AI search to our app"
Agent: Implement semantic search with embeddings
User: "Build a chatbot for support"
Agent: Create RAG-based chatbot with knowledge base
User: "Our LLM costs are too high"
Agent: Optimize with caching, smaller model, few-shot
User: "Add content generation to our app"
Agent: Integrate LLM with appropriate guardrails
User: "Make recommendations smarter"
Agent: Add ML recommendation model
Compatible Agents
All SKILL.md compatible agents ✅
See Also
- web-developer (implementation)
- data-analytics (metrics)
