Week 12 Session 3: Real-World LLM Applications#
In this final session of Week 12, we’ll explore practical applications and advanced use cases for controlling and structuring LLM outputs, focusing on real-world implementations and complex scenarios.
Learning Objectives#
Apply structured output techniques to real-world use cases
Implement complex validation workflows
Design robust error handling systems
Create production-ready LLM applications
Real-World Applications#
1. Structured Information Extraction#
class InformationExtractor:
def __init__(self):
self.llm = LLMController()
def extract_entities(self, text: str) -> dict:
prompt = f"""
Extract key entities from the following text and format as JSON:
{text}
Required format:
{{
"people": [{"name": str, "role": str}],
"organizations": [{"name": str, "type": str}],
"locations": [{"name": str, "context": str}]
}}
"""
return self.llm.generate_controlled_output(
prompt,
temperature=0.3 # Lower temperature for factual extraction
)
2. Automated Report Generation#
class ReportGenerator:
def __init__(self):
self.llm = LLMController()
self.template = """
# Analysis Report
## Executive Summary
{summary}
## Key Findings
{findings}
## Recommendations
{recommendations}
## Technical Details
{details}
"""
def generate_report(self, data: dict) -> str:
sections = {}
for section in ['summary', 'findings', 'recommendations', 'details']:
prompt = f"Generate {section} section for report about: {data['topic']}"
sections[section] = self.llm.generate_controlled_output(
prompt,
format_type='markdown'
)
return self.template.format(**sections)
Complex Validation Workflows#
1. Multi-Stage Validation Pipeline#
class ValidationPipeline:
def __init__(self):
self.stages = []
def add_stage(self, validator, error_handler=None):
self.stages.append({
'validator': validator,
'error_handler': error_handler or self.default_handler
})
async def process(self, content):
result = content
for stage in self.stages:
try:
if not stage['validator'](result):
result = await stage['error_handler'](result)
except Exception as e:
result = await stage['error_handler'](result, error=e)
return result
@staticmethod
async def default_handler(content, error=None):
logger.error(f"Validation failed: {error}")
raise ValueError("Content failed validation")
2. Context-Aware Validation#
class ContextualValidator:
def __init__(self):
self.context = {}
self.rules = {}
def add_context(self, key: str, value: any):
self.context[key] = value
def add_rule(self, name: str, rule_func):
self.rules[name] = rule_func
def validate(self, content: dict) -> bool:
for rule_name, rule_func in self.rules.items():
if not rule_func(content, self.context):
logger.error(f"Rule {rule_name} failed")
return False
return True
# Example usage
validator = ContextualValidator()
validator.add_context('max_length', 1000)
validator.add_context('required_fields', ['title', 'content'])
validator.add_rule('length_check',
lambda x, ctx: len(str(x.get('content', ''))) <= ctx['max_length'])
Production-Ready Implementation#
1. Scalable LLM Service#
from fastapi import FastAPI, HTTPException
from pydantic import BaseModel
from typing import Dict, Any
app = FastAPI()
class LLMService:
def __init__(self):
self.controller = LLMController()
self.cache = {}
async def generate(self,
prompt: str,
params: Dict[str, Any]) -> str:
cache_key = self._get_cache_key(prompt, params)
if cache_key in self.cache:
return self.cache[cache_key]
try:
result = await self.controller.generate_controlled_output(
prompt=prompt,
**params
)
self.cache[cache_key] = result
return result
except Exception as e:
logger.error(f"Generation failed: {e}")
raise HTTPException(
status_code=500,
detail="Failed to generate response"
)
def _get_cache_key(self, prompt: str, params: Dict[str, Any]) -> str:
return f"{hash(prompt)}_{hash(str(params))}"
service = LLMService()
@app.post("/generate")
async def generate_endpoint(request: dict):
return await service.generate(
request['prompt'],
request.get('params', {})
)
2. Monitoring and Analytics#
class LLMMonitor:
def __init__(self):
self.metrics = defaultdict(Counter)
self.latencies = defaultdict(list)
async def track_request(self,
request_type: str,
start_time: float):
duration = time.time() - start_time
self.latencies[request_type].append(duration)
self.metrics[request_type]['total_requests'] += 1
def get_statistics(self) -> dict:
stats = {}
for req_type in self.metrics:
stats[req_type] = {
'total_requests': self.metrics[req_type]['total_requests'],
'avg_latency': statistics.mean(self.latencies[req_type]),
'p95_latency': statistics.quantiles(
self.latencies[req_type],
n=20
)[18] # 95th percentile
}
return stats
Best Practices for Production Deployment#
Performance Optimization
Implement caching strategies
Use async/await for I/O operations
Batch similar requests when possible
Error Handling
Implement circuit breakers
Use fallback responses
Log errors with context
Monitoring
Track response times
Monitor error rates
Set up alerts for anomalies
Security
Validate all inputs
Rate limit requests
Implement authentication
Practical Exercise#
Implement a complete LLM service with the following features:
Structured output generation
Multi-stage validation
Error handling and recovery
Monitoring and analytics
Caching mechanism
Next Steps#
As we conclude Week 12, you should now have a comprehensive understanding of:
Output structuring techniques
Control mechanisms
Validation strategies
Production deployment considerations
Apply these concepts in your final projects, focusing on creating robust and production-ready LLM applications.
References#
“Building Production-Ready LLM Applications” - AWS Machine Learning Blog
“Scaling Language Models in Production” - Microsoft Research
“Best Practices for LLM API Design” - OpenAI Engineering
“Monitoring ML Systems” - Google SRE Handbook