claude-skills-reference/engineering-team/senior-ml-engineer/references/llm_integration_guide.md

LLM Integration Guide

Production patterns for integrating Large Language Models into applications.

---

Table of Contents

• API Integration Patterns
• Prompt Engineering
• Token Optimization
• Cost Management
• Error Handling

---

API Integration Patterns

Provider Abstraction Layer

from abc import ABC, abstractmethod
from typing import List, Dict, Any

class LLMProvider(ABC):
    """Abstract base class for LLM providers."""

    @abstractmethod
    def complete(self, prompt: str, **kwargs) -> str:
        pass

    @abstractmethod
    def chat(self, messages: List[Dict], **kwargs) -> str:
        pass

class OpenAIProvider(LLMProvider):
    def __init__(self, api_key: str, model: str = "gpt-4"):
        self.client = OpenAI(api_key=api_key)
        self.model = model

    def complete(self, prompt: str, **kwargs) -> str:
        response = self.client.completions.create(
            model=self.model,
            prompt=prompt,
            **kwargs
        )
        return response.choices[0].text

class AnthropicProvider(LLMProvider):
    def __init__(self, api_key: str, model: str = "claude-3-opus"):
        self.client = Anthropic(api_key=api_key)
        self.model = model

    def chat(self, messages: List[Dict], **kwargs) -> str:
        response = self.client.messages.create(
            model=self.model,
            messages=messages,
            **kwargs
        )
        return response.content[0].text

Retry and Fallback Strategy

import time
from tenacity import retry, stop_after_attempt, wait_exponential

@retry(
    stop=stop_after_attempt(3),
    wait=wait_exponential(multiplier=1, min=1, max=10)
)
def call_llm_with_retry(provider: LLMProvider, prompt: str) -> str:
    """Call LLM with exponential backoff retry."""
    return provider.complete(prompt)

def call_with_fallback(
    primary: LLMProvider,
    fallback: LLMProvider,
    prompt: str
) -> str:
    """Try primary provider, fall back on failure."""
    try:
        return call_llm_with_retry(primary, prompt)
    except Exception as e:
        logger.warning(f"Primary provider failed: {e}, using fallback")
        return call_llm_with_retry(fallback, prompt)

---

Prompt Engineering

Prompt Templates

Pattern	Use Case	Structure
Zero-shot	Simple tasks	Task description + input
Few-shot	Complex tasks	Examples + task + input
Chain-of-thought	Reasoning	"Think step by step" + task
Role-based	Specialized output	System role + task

Few-Shot Template

FEW_SHOT_TEMPLATE = """
You are a sentiment classifier. Classify the sentiment as positive, negative, or neutral.

Examples:
Input: "This product is amazing, I love it!"
Output: positive

Input: "Terrible experience, waste of money."
Output: negative

Input: "The product arrived on time."
Output: neutral

Now classify:
Input: "{user_input}"
Output:"""

def classify_sentiment(text: str, provider: LLMProvider) -> str:
    prompt = FEW_SHOT_TEMPLATE.format(user_input=text)
    response = provider.complete(prompt, max_tokens=10, temperature=0)
    return response.strip().lower()

System Prompts for Consistency

SYSTEM_PROMPT = """You are a helpful assistant that answers questions about our product.

Guidelines:
- Be concise and direct
- Use bullet points for lists
- If unsure, say "I don't have that information"
- Never make up information
- Keep responses under 200 words

Product context:
{product_context}
"""

def create_chat_messages(user_query: str, context: str) -> List[Dict]:
    return [
        {"role": "system", "content": SYSTEM_PROMPT.format(product_context=context)},
        {"role": "user", "content": user_query}
    ]

---

Token Optimization

Token Counting

import tiktoken

def count_tokens(text: str, model: str = "gpt-4") -> int:
    """Count tokens for a given text and model."""
    encoding = tiktoken.encoding_for_model(model)
    return len(encoding.encode(text))

def truncate_to_token_limit(text: str, max_tokens: int, model: str = "gpt-4") -> str:
    """Truncate text to fit within token limit."""
    encoding = tiktoken.encoding_for_model(model)
    tokens = encoding.encode(text)

    if len(tokens) <= max_tokens:
        return text

    return encoding.decode(tokens[:max_tokens])

Context Window Management

Model	Context Window	Effective Limit
GPT-4	8,192	~6,000 (leave room for response)
GPT-4-32k	32,768	~28,000
Claude 3	200,000	~180,000
Llama 3	8,192	~6,000

Chunking Strategy

def chunk_text(text: str, chunk_size: int = 1000, overlap: int = 100) -> List[str]:
    """Split text into overlapping chunks."""
    chunks = []
    start = 0

    while start < len(text):
        end = start + chunk_size
        chunk = text[start:end]
        chunks.append(chunk)
        start = end - overlap

    return chunks

---

Cost Management

Cost Calculation

Provider	Input Cost	Output Cost	Example (1K tokens)
GPT-4	$0.03/1K	$0.06/1K	$0.09
GPT-3.5	$0.0005/1K	$0.0015/1K	$0.002
Claude 3 Opus	$0.015/1K	$0.075/1K	$0.09
Claude 3 Haiku	$0.00025/1K	$0.00125/1K	$0.0015

Cost Tracking

from dataclasses import dataclass
from typing import Optional

@dataclass
class LLMUsage:
    input_tokens: int
    output_tokens: int
    model: str
    cost: float

def calculate_cost(
    input_tokens: int,
    output_tokens: int,
    model: str
) -> float:
    """Calculate cost based on token usage."""
    PRICING = {
        "gpt-4": {"input": 0.03, "output": 0.06},
        "gpt-3.5-turbo": {"input": 0.0005, "output": 0.0015},
        "claude-3-opus": {"input": 0.015, "output": 0.075},
    }

    prices = PRICING.get(model, {"input": 0.01, "output": 0.03})

    input_cost = (input_tokens / 1000) * prices["input"]
    output_cost = (output_tokens / 1000) * prices["output"]

    return input_cost + output_cost

Cost Optimization Strategies

1. Use smaller models for simple tasks - GPT-3.5 for classification, GPT-4 for reasoning
2. Cache common responses - Store results for repeated queries
3. Batch requests - Combine multiple items in single prompt
4. Truncate context - Only include relevant information
5. Set max_tokens limit - Prevent runaway responses

---

Error Handling

Common Error Types

Error	Cause	Handling
RateLimitError	Too many requests	Exponential backoff
InvalidRequestError	Bad input	Validate before sending
AuthenticationError	Invalid API key	Check credentials
ServiceUnavailable	Provider down	Fallback to alternative
ContextLengthExceeded	Input too long	Truncate or chunk

Error Handling Pattern

from openai import RateLimitError, APIError

def safe_llm_call(provider: LLMProvider, prompt: str, max_retries: int = 3) -> str:
    """Safely call LLM with comprehensive error handling."""
    for attempt in range(max_retries):
        try:
            return provider.complete(prompt)

        except RateLimitError:
            wait_time = 2 ** attempt
            logger.warning(f"Rate limited, waiting {wait_time}s")
            time.sleep(wait_time)

        except APIError as e:
            if e.status_code >= 500:
                logger.warning(f"Server error: {e}, retrying...")
                time.sleep(1)
            else:
                raise

    raise Exception(f"Failed after {max_retries} attempts")

Response Validation

import json
from pydantic import BaseModel, ValidationError

class StructuredResponse(BaseModel):
    answer: str
    confidence: float
    sources: List[str]

def parse_structured_response(response: str) -> StructuredResponse:
    """Parse and validate LLM JSON response."""
    try:
        data = json.loads(response)
        return StructuredResponse(**data)
    except json.JSONDecodeError:
        raise ValueError("Response is not valid JSON")
    except ValidationError as e:
        raise ValueError(f"Response validation failed: {e}")