personamanagmentlayer__pcl/stdlib/ai/ai-engineer-expert/SKILL.md

name, version, description, category, tags, allowed-tools

name	version	description	category	tags	allowed-tools
ai-engineer-expert	1.1.0	Expert-level AI implementation, deployment, LLM integration, and production AI systems. Use when the user mentions AI engineering, LLM, deployment, production AI, or integration, or when the task involves LLM Patterns, LLM Integration, or Production Systems.	ai	ai-engineering llm deployment production-ai integration	Read Write Edit Bash(python:*)

AI Engineer Expert

Expert guidance for implementing AI systems, LLM integration, prompt engineering, and deploying production AI applications.

Core Concepts

AI Engineering

• LLM integration and orchestration
• Prompt engineering and optimization
• RAG (Retrieval-Augmented Generation)
• Vector databases and embeddings
• Fine-tuning and adaptation
• AI agent systems

Production AI

• Model deployment strategies
• API design for AI services
• Rate limiting and cost control
• Error handling and fallbacks
• Monitoring and logging
• Security and safety

LLM Patterns

• Chain-of-thought prompting
• Few-shot learning
• System/user message design
• Function calling and tools
• Streaming responses
• Context window management

LLM Integration

from openai import AsyncOpenAI
from anthropic import Anthropic
from typing import List, Dict, Optional
import asyncio

class LLMClient:
    """Unified LLM client with fallback"""

    def __init__(self, primary: str = "openai", fallback: str = "anthropic"):
        self.openai_client = AsyncOpenAI()
        self.anthropic_client = Anthropic()
        self.primary = primary
        self.fallback = fallback

    async def chat_completion(self, messages: List[Dict],
                              model: str = "gpt-4-turbo",
                              temperature: float = 0.7,
                              max_tokens: int = 1000) -> str:
        """Chat completion with fallback"""
        try:
            if self.primary == "openai":
                response = await self.openai_client.chat.completions.create(
                    model=model,
                    messages=messages,
                    temperature=temperature,
                    max_tokens=max_tokens
                )
                return response.choices[0].message.content

        except Exception as e:
            print(f"Primary provider failed: {e}, trying fallback")

            if self.fallback == "anthropic":
                response = self.anthropic_client.messages.create(
                    model="claude-3-5-sonnet-20241022",
                    messages=messages,
                    temperature=temperature,
                    max_tokens=max_tokens
                )
                return response.content[0].text

    async def chat_completion_streaming(self, messages: List[Dict],
                                       model: str = "gpt-4-turbo"):
        """Streaming chat completion"""
        stream = await self.openai_client.chat.completions.create(
            model=model,
            messages=messages,
            stream=True
        )

        async for chunk in stream:
            if chunk.choices[0].delta.content:
                yield chunk.choices[0].delta.content

    async def function_calling(self, messages: List[Dict],
                              tools: List[Dict]) -> Dict:
        """Function calling with tools"""
        response = await self.openai_client.chat.completions.create(
            model="gpt-4-turbo",
            messages=messages,
            tools=tools,
            tool_choice="auto"
        )

        message = response.choices[0].message

        if message.tool_calls:
            return {
                "type": "function_call",
                "function": message.tool_calls[0].function.name,
                "arguments": message.tool_calls[0].function.arguments
            }
        else:
            return {
                "type": "message",
                "content": message.content
            }

RAG Implementation

from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain_openai import OpenAIEmbeddings
from langchain_community.vectorstores import Chroma
from langchain.chains import RetrievalQA
from langchain_openai import ChatOpenAI

class RAGSystem:
    """Retrieval-Augmented Generation system"""

    def __init__(self, persist_directory: str = "./chroma_db"):
        self.embeddings = OpenAIEmbeddings()
        self.vectorstore = None
        self.persist_directory = persist_directory
        self.llm = ChatOpenAI(model="gpt-4-turbo", temperature=0)

    def ingest_documents(self, documents: List[str]):
        """Ingest and index documents"""
        # Split documents into chunks
        text_splitter = RecursiveCharacterTextSplitter(
            chunk_size=1000,
            chunk_overlap=200
        )
        chunks = text_splitter.create_documents(documents)

        # Create vector store
        self.vectorstore = Chroma.from_documents(
            documents=chunks,
            embedding=self.embeddings,
            persist_directory=self.persist_directory
        )

    def query(self, question: str, k: int = 4) -> Dict:
        """Query with RAG"""
        if not self.vectorstore:
            raise ValueError("No documents ingested")

        # Retrieve relevant documents
        retriever = self.vectorstore.as_retriever(
            search_kwargs={"k": k}
        )

        # Create QA chain
        qa_chain = RetrievalQA.from_chain_type(
            llm=self.llm,
            chain_type="stuff",
            retriever=retriever,
            return_source_documents=True
        )

        # Get answer
        result = qa_chain({"query": question})

        return {
            "answer": result["result"],
            "sources": [doc.page_content for doc in result["source_documents"]]
        }

    def similarity_search(self, query: str, k: int = 4) -> List[Dict]:
        """Similarity search in vector database"""
        results = self.vectorstore.similarity_search_with_score(query, k=k)

        return [
            {
                "content": doc.page_content,
                "score": score,
                "metadata": doc.metadata
            }
            for doc, score in results
        ]

Prompt Engineering

class PromptTemplate:
    """Advanced prompt templates"""

    @staticmethod
    def chain_of_thought(question: str) -> str:
        """Chain-of-thought prompting"""
        return f"""Let's solve this step by step:

Question: {question}

Please think through this problem carefully:
1. First, identify what we need to find
2. Then, break down the problem into smaller steps
3. Solve each step
4. Finally, combine the results

Your step-by-step solution:"""

    @staticmethod
    def few_shot(task: str, examples: List[Dict], query: str) -> str:
        """Few-shot learning prompt"""
        examples_text = "\n\n".join([
            f"Input: {ex['input']}\nOutput: {ex['output']}"
            for ex in examples
        ])

        return f"""Task: {task}

Here are some examples:

{examples_text}

Now, please solve this:

Input: {query}
Output:"""

    @staticmethod
    def system_message(role: str, constraints: List[str],
                       format_instructions: str) -> str:
        """System message template"""
        constraints_text = "\n".join([f"- {c}" for c in constraints])

        return f"""You are a {role}.

Constraints:
{constraints_text}

Output Format:
{format_instructions}

Remember to follow these guidelines strictly."""

AI Agent System

from typing import Callable
import json

class Tool:
    """Tool that agents can use"""

    def __init__(self, name: str, description: str, function: Callable):
        self.name = name
        self.description = description
        self.function = function

    def to_openai_function(self) -> Dict:
        """Convert to OpenAI function format"""
        return {
            "type": "function",
            "function": {
                "name": self.name,
                "description": self.description,
                "parameters": self.get_parameters()
            }
        }

class AIAgent:
    """AI agent with tools"""

    def __init__(self, llm_client: LLMClient, tools: List[Tool]):
        self.llm = llm_client
        self.tools = {tool.name: tool for tool in tools}
        self.conversation_history = []

    async def run(self, user_input: str, max_iterations: int = 10) -> str:
        """Run agent with tool use"""
        self.conversation_history.append({
            "role": "user",
            "content": user_input
        })

        for i in range(max_iterations):
            # Get LLM response with function calling
            response = await self.llm.function_calling(
                messages=self.conversation_history,
                tools=[tool.to_openai_function() for tool in self.tools.values()]
            )

            if response["type"] == "message":
                # Agent is done
                return response["content"]

            # Execute tool
            tool_name = response["function"]
            arguments = json.loads(response["arguments"])

            tool_result = await self.execute_tool(tool_name, arguments)

            # Add tool result to conversation
            self.conversation_history.append({
                "role": "function",
                "name": tool_name,
                "content": str(tool_result)
            })

        return "Max iterations reached"

    async def execute_tool(self, tool_name: str, arguments: Dict) -> any:
        """Execute a tool"""
        if tool_name not in self.tools:
            raise ValueError(f"Tool {tool_name} not found")

        tool = self.tools[tool_name]
        return await tool.function(**arguments)

Production Deployment

from fastapi import FastAPI, HTTPException, Depends
from fastapi.responses import StreamingResponse
from pydantic import BaseModel
from circuitbreaker import circuit
import asyncio

app = FastAPI()

class ChatRequest(BaseModel):
    messages: List[Dict]
    model: str = "gpt-4-turbo"
    stream: bool = False

class RateLimiter:
    """Rate limiter for API"""

    def __init__(self, max_requests: int, window_seconds: int):
        self.max_requests = max_requests
        self.window_seconds = window_seconds
        self.requests = {}

    async def check_limit(self, user_id: str) -> bool:
        """Check if user is within rate limit"""
        import time
        now = time.time()

        if user_id not in self.requests:
            self.requests[user_id] = []

        # Remove old requests
        self.requests[user_id] = [
            req_time for req_time in self.requests[user_id]
            if now - req_time < self.window_seconds
        ]

        if len(self.requests[user_id]) >= self.max_requests:
            return False

        self.requests[user_id].append(now)
        return True

rate_limiter = RateLimiter(max_requests=100, window_seconds=60)
llm_client = LLMClient()

@circuit(failure_threshold=5, recovery_timeout=60)
async def call_llm(messages: List[Dict]) -> str:
    """LLM call with circuit breaker"""
    return await llm_client.chat_completion(messages)

@app.post("/chat")
async def chat(request: ChatRequest, user_id: str = Depends(get_user_id)):
    """Chat endpoint with rate limiting"""
    # Check rate limit
    if not await rate_limiter.check_limit(user_id):
        raise HTTPException(status_code=429, detail="Rate limit exceeded")

    try:
        if request.stream:
            async def generate():
                async for chunk in llm_client.chat_completion_streaming(request.messages):
                    yield chunk

            return StreamingResponse(generate(), media_type="text/event-stream")
        else:
            response = await call_llm(request.messages)
            return {"response": response}

    except Exception as e:
        raise HTTPException(status_code=500, detail=str(e))

Best Practices

LLM Integration

• Implement fallback providers
• Use streaming for better UX
• Cache responses where appropriate
• Handle rate limits gracefully
• Monitor token usage and costs
• Version prompts and track changes

Production Systems

• Implement circuit breakers
• Add comprehensive logging
• Monitor latency and errors
• Use rate limiting
• Implement retry logic with backoff
• Test edge cases thoroughly

Security

• Validate and sanitize inputs
• Implement authentication/authorization
• Never expose API keys in logs
• Use environment variables for secrets
• Implement content filtering
• Monitor for prompt injection

Anti-Patterns

❌ No error handling or fallbacks ❌ Exposing raw LLM outputs without validation ❌ No rate limiting or cost controls ❌ Storing API keys in code ❌ No monitoring or logging ❌ Ignoring token limits ❌ No testing of prompts

Resources

• OpenAI API: https://platform.openai.com/docs
• Anthropic Claude: https://docs.anthropic.com/
• LangChain: https://python.langchain.com/
• LlamaIndex: https://www.llamaindex.ai/
• Weights & Biases Prompts: https://wandb.ai/site/prompts