Chapter 8: LLM Applications & Best Practices
Production Deployment
Learning Objectives
- Understand llm applications & best practices fundamentals
- Master the mathematical foundations
- Learn practical implementation
- Apply knowledge through examples
- Recognize real-world applications
LLM Applications & Best Practices
Introduction
Production Deployment
This chapter provides comprehensive coverage of llm applications & best practices, including detailed explanations, mathematical formulations, code implementations, and real-world examples.
📚 Why This Matters
Understanding llm applications & best practices is crucial for mastering modern AI systems. This chapter breaks down complex concepts into digestible explanations with step-by-step examples.
Key Concepts
LLM Application Patterns
Direct generation: Model generates output directly from prompt. Used for creative writing, code generation, summarization.
Classification: Model outputs class label. Used for sentiment analysis, content moderation, spam detection.
Extraction: Model extracts structured information. Used for named entity recognition, data extraction, question answering.
Transformation: Model transforms input format. Used for translation, reformatting, style transfer.
Best Practices for LLM Applications
Prompt design: Clear, specific, well-structured prompts significantly improve results.
Error handling: LLMs can fail or produce unexpected outputs. Implement validation, retries, and fallbacks.
Cost optimization: Use smaller models when possible, cache responses, batch requests, use efficient sampling.
Safety and monitoring: Filter harmful content, monitor for bias, track usage and costs, implement rate limiting.
Deployment Considerations
Latency: Large models are slow. Consider model size, batching, caching, and optimization techniques.
Cost: API costs scale with usage. Monitor token usage, optimize prompts, consider fine-tuning for efficiency.
Reliability: Implement retries, fallbacks, and error handling. LLMs can be non-deterministic.
Mathematical Formulations
Token Cost Calculation
Where:
- Input tokens: Prompt length
- Output tokens: Generated text length
- Price varies by model (GPT-4 more expensive than GPT-3.5)
- Optimize by reducing prompt size and output length
Temperature Sampling
Where:
- \(T\): Temperature parameter
- \(T < 1\): Sharper distribution (more deterministic)
- \(T > 1\): Flatter distribution (more random)
- \(T = 1\): Standard softmax
Top-k Sampling
Only consider top k tokens by probability. Filters out low-probability tokens to improve quality while maintaining diversity.
Detailed Examples
Example: Building a Chatbot
Step 1: Define system prompt
You are a helpful assistant. Be concise, friendly, and accurate.
Step 2: Handle conversation
- Maintain conversation history
- Format: [system] + [history] + [user message]
- Generate response
- Update history
Step 3: Add safety checks
- Filter harmful content
- Validate responses
- Implement rate limiting
Example: Cost Optimization
Scenario: Processing 10,000 documents
Without optimization:
- Average prompt: 500 tokens
- Average output: 200 tokens
- Cost: 10,000 × 700 × $0.002 = $14,000
With optimization:
- Reduce prompt to 200 tokens (remove unnecessary context)
- Limit output to 100 tokens (use max_tokens)
- Cost: 10,000 × 300 × $0.002 = $6,000 (57% reduction!)
Implementation
LLM Application with Error Handling
from transformers import pipeline
import time
from typing import Optional
class LLMApplication:
"""LLM application with error handling and retries"""
def __init__(self, model_name="gpt2", max_retries=3):
self.generator = pipeline("text-generation", model=model_name)
self.max_retries = max_retries
def generate_with_retry(self, prompt: str, max_length: int = 100) -> Optional[str]:
"""Generate text with retry logic"""
for attempt in range(self.max_retries):
try:
result = self.generator(
prompt,
max_length=max_length,
num_return_sequences=1,
temperature=0.7,
do_sample=True
)
return result[0]['generated_text']
except Exception as e:
print(f"Attempt {attempt + 1} failed: {e}")
if attempt < self.max_retries - 1:
time.sleep(2 ** attempt) # Exponential backoff
else:
return None
return None
def validate_output(self, output: str) -> bool:
"""Validate generated output"""
# Add validation logic
if len(output) < 10:
return False
if any(word in output.lower() for word in ["error", "invalid"]):
return False
return True
# Example usage
app = LLMApplication()
result = app.generate_with_retry("The capital of France is")
if result and app.validate_output(result):
print(result)Cost Tracking
class CostTracker:
"""Track token usage and costs"""
def __init__(self, price_per_1k_tokens=0.002):
self.price_per_1k = price_per_1k_tokens
self.total_input_tokens = 0
self.total_output_tokens = 0
def record_usage(self, input_tokens: int, output_tokens: int):
"""Record token usage"""
self.total_input_tokens += input_tokens
self.total_output_tokens += output_tokens
def get_cost(self) -> float:
"""Calculate total cost"""
total_tokens = self.total_input_tokens + self.total_output_tokens
return (total_tokens / 1000) * self.price_per_1k
def get_stats(self) -> dict:
"""Get usage statistics"""
return {
"input_tokens": self.total_input_tokens,
"output_tokens": self.total_output_tokens,
"total_tokens": self.total_input_tokens + self.total_output_tokens,
"cost": self.get_cost()
}
# Example
tracker = CostTracker()
tracker.record_usage(500, 200)
print(tracker.get_stats())Real-World Applications
Major LLM Applications
Content Creation:
- Writing assistance (Grammarly, Jasper)
- Marketing copy generation
- Blog post and article writing
- Social media content
Customer Service:
- Chatbots for support
- Email response generation
- FAQ automation
- Ticket classification and routing
Software Development:
- Code completion (GitHub Copilot)
- Code generation from descriptions
- Documentation generation
- Code review and debugging assistance
Best Practices Summary
Design: Clear prompts, proper formatting, relevant examples
Performance: Optimize prompts, use appropriate models, implement caching
Reliability: Error handling, validation, retries, fallbacks
Safety: Content filtering, bias monitoring, rate limiting
Cost: Monitor usage, optimize prompts, choose right model size