A case study detailing lessons learned from processing over 250k LLM calls on 100k corporate documents at Credal. The team discovered that successful LLM implementations require careful data formatting and focused prompt engineering. Key findings included the importance of structuring data to maximize LLM understanding, especially for complex documents with footnotes and tables, and concentrating prompts on the most challenging aspects of tasks rather than trying to solve multiple problems simultaneously.
# Credal's Journey in Building Production LLM Systems
This case study details the experiences and lessons learned by Credal, a company specializing in helping enterprises safely use their data with Generative AI. The insights come from processing over 250,000 LLM calls across 100,000 corporate documents, offering valuable perspectives on building production-ready LLM systems.
# Core Technical Challenges and Solutions
## Data Formatting for LLM Understanding
- **Challenge with Complex Documents**
- **Solution: Enhanced Data Preprocessing**
- **Implementation Details**
## Prompt Engineering Optimization
- **Challenge with Complex Tasks**
- **Solution: Task-Focused Prompting**
- **Implementation Details**
# Technical Architecture Components
## Vector Store Implementation
- **Document Processing**
- **Search Optimization**
## Cost Optimization Strategies
- **Model Selection**
- **Processing Optimization**
# Production Deployment Insights
## Scaling Considerations
- **Document Volume Management**
- **Performance Optimization**
## Enterprise Integration
- **Security and Compliance**
- **User Experience**
# Key Learnings and Best Practices
## Data Preparation
- **Document Processing**
- **Context Management**
## Prompt Engineering
- **Design Principles**
- **Implementation Strategies**
# Results and Impact
## System Performance
- **Accuracy Improvements**
- **Cost Efficiency**
## Business Value
- **Enterprise Integration**
- **Scalability**
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