LLM Application Casebook: From Theory to Practice
Through real success stories, see how LLMs create value across industries. Each case includes detailed implementation steps and key metrics to help you land AI applications quickly.
Financial Services Case
🏦 Intelligent Customer Service for a Major Bank
Project Background
- •Challenge: Over 100,000 daily inquiries; high labor costs
- •Goal: Automate 80% of FAQs; improve satisfaction
- •Scale: Serving 20M+ users
Technical Solution
- • Foundation models: GPT-4 + in-house finance model
- • RAG system: 500k+ knowledge base documents
- • Multi-turn conversation management
- • Sentiment analysis and escalation
📊 Outcomes
87%
Resolution rate
4.6/5
User satisfaction
65%
Cost reduction
24/7
Service availability
💡 Key Learnings
- • High accuracy is essential in finance; hybrid models work best
- • A robust human handoff mechanism is critical
- • Continuous knowledge base updates are key to sustained performance
E-commerce Retail Case
🛍️ Cross-border Personalized Recommendation System
Business Scenario
A cross-border e-commerce platform with 5M SKUs serving users in 30+ countries. Traditional recommendation algorithms struggled to capture fine-grained product differences and cultural preferences.
Model Architecture
- • Multilingual product understanding
- • User intent recognition
- • Cross-cultural preference modeling
- • Real-time personalization
Technical Innovations
- • Multimodal fusion
- • Few-shot learning
- • Online learning updates
- • A/B testing framework
Business Impact
- • CTR +42%
- • Conversion rate +28%
- • Average order value +35%
- • Repeat purchase rate +20%
Implementation Details
# Recommendation system architecture example
class MultiModalRecommender:
def __init__(self):
self.text_encoder = LLMEncoder('multilingual-e5')
self.image_encoder = CLIPModel()
self.user_model = UserPreferenceModel()
def generate_recommendations(self, user_id, context):
# 1. Understand user intent
user_profile = self.user_model.get_profile(user_id)
intent = self.analyze_intent(context, user_profile)
# 2. Multimodal product retrieval
text_query = self.text_encoder.encode(intent.query)
visual_preferences = self.extract_visual_prefs(user_profile)
# 3. Cross-cultural adjustment
cultural_factors = self.get_cultural_factors(
user_profile.country,
user_profile.language
)
# 4. Personalized ranking
candidates = self.retrieve_candidates(
text_query,
visual_preferences,
cultural_factors
)
return self.personalized_ranking(candidates, user_profile)Healthcare Case
🏥 Medical Imaging Assisted Diagnosis
⚠️ Disclaimer: AI is for assistance only. Final diagnosis must be confirmed by a licensed physician.
Project Overview
Application scenario
Lung CT analysis to assist pulmonary nodule detection
Partner hospitals
15 tertiary hospitals across 5 provinces
Data scale
1M+ labeled images with continuous updates
Clinical Outcomes
Technical Highlights
- • 3D convolutional neural networks
- • Attention mechanisms to localize lesions
- • Multi-scale feature fusion
- • Explainability heatmaps
- • Confidence scoring
- • Continual learning
Education Case
🎓 K-12 Intelligent Teaching Assistant
Product Positioning
Personalized tutoring for K-12 students in math, physics, chemistry, and more. AI identifies weak knowledge areas and provides targeted practice and explanations.
Core Features
- 📝Smart Q&A: Upload a photo of the question for detailed AI explanations
- 📊Knowledge diagnosis: Analyze wrong answers and locate blind spots
- 🎯Personalized practice: Recommend questions based on ability models
- 📈Learning reports: Visualize progress over time
Results
1.5M+
Monthly active users
23%
Average grade improvement
4.8/5
Parent satisfaction
Success Factors
- • Strict content review to ensure teaching quality
- • Gamified design to increase engagement
- • Parent portal app for transparent progress
- • Tight alignment with school curricula
Creative Content Case
🎨 AI-assisted Game Development Platform
Scene generation
Text descriptions → 3D scenes
NPC dialogue
Intelligent conversation systems
Story writing
Branching narrative generation
Project Results
500+
Game studios
80%
Development time saved
100k+
Generated assets
3
Hit titles
Legal Services Case
⚖️ Intelligent Legal Document Assistant
Solution
AI-powered document generation system for law firms supporting contracts, legal opinions, litigation documents, and more—significantly boosting attorney productivity.
Technical Implementation
# Legal document generation flow
def generate_legal_document(case_info):
# 1. Case analysis
case_type = classify_case(case_info)
relevant_laws = retrieve_laws(case_type)
# 2. Template selection
template = select_template(
case_type,
case_info.jurisdiction
)
# 3. Content generation
document = llm.generate(
template=template,
case_facts=case_info.facts,
legal_basis=relevant_laws,
style='formal_legal'
)
# 4. Compliance check
compliance_check(document)
return documentImpact
Manufacturing Case
🏭 Intelligent Quality Inspection
Application Background
An electronics manufacturer producing 10M phone accessories annually had low efficiency and high miss rates in manual QA. An AI vision inspection system was introduced to automatically detect product defects.
99.8%
Detection accuracy
0.02%
Miss rate
10×
Speed improvement
¥8M
Annual cost savings
Key Techniques
- • Multi-angle image acquisition
- • Automatic defect classification
- • Real-time edge deployment
- • Adaptive thresholding
Implementation Takeaways
Common Traits of Successful Projects
🎯 Clear Business Goals
- • Concrete, measurable KPIs
- • Alignment with strategy
- • Clear ROI expectations
- • Milestones by phase
👥 Cross-functional Collaboration
- • Business–tech synergy
- • Executive sponsorship
- • Deep user involvement
- • Ongoing communication
🔄 Iterative Optimization
- • Small, fast iterations
- • Data-driven decisions
- • Rapid experimentation
- • Continuous improvement
Start Your AI Application Journey
Learn from successful examples and tailor them to your context to build flagship AI applications.
Start Now