Introduction
At SETA International Vietnam, I built a QnA bot leveraging Google's Gemini LLM to help engineers search through 100,000+ documents across Confluence, Jira, and Zendesk. This experience taught me valuable lessons about deploying and maintaining LLMs in production environments. Here's what I learned along the way.
The Challenge
Engineers were spending hours searching for documentation across multiple platforms. We needed a solution that could:
- Understand natural language queries
- Search across diverse data sources
- Provide accurate, contextual answers
- Scale to handle hundreds of concurrent users
- Stay within reasonable cost constraints
Architecture Design
Data Ingestion Pipeline
The first step was building a robust data pipeline to ingest documents from various sources:
- Webhooks: Real-time updates for new/modified documents
- Batch Processing: Scheduled full syncs to catch any missed updates
- Data Transformation: Standardizing formats across different source systems
- Vector Embeddings: Converting documents into searchable vector representations
Retrieval-Augmented Generation (RAG)
We implemented a RAG architecture to enhance the LLM's responses with relevant context:
- User query is converted to vector embeddings
- Semantic search finds the most relevant documents
- Retrieved documents are included in the LLM prompt
- LLM generates answer based on provided context
Prompt Engineering
Effective prompt engineering was crucial for getting accurate, helpful responses. Key strategies:
System Prompts
We defined clear system prompts that instructed the model to:
- Only answer based on provided context
- Cite sources for all information
- Admit when it doesn't know the answer
- Maintain a helpful, professional tone
Few-Shot Learning
Including examples of good question-answer pairs in the prompt significantly improved response quality, especially for domain-specific queries.
Context Management
Managing context windows effectively was one of the biggest challenges:
Chunking Strategy
- Split documents into semantic chunks (paragraphs, sections)
- Maintain overlap between chunks to preserve context
- Store metadata (source, title, date) with each chunk
Context Ranking
Not all retrieved documents are equally relevant. We implemented a ranking system that considers:
- Semantic similarity score
- Document recency
- Source reliability
- User feedback (thumbs up/down)
Cost Optimization
LLM API calls can get expensive quickly. Here's how we managed costs:
Caching Strategy
- Cache responses for identical queries
- Implement semantic caching for similar queries
- Cache embeddings for frequent documents
Smart Token Management
- Truncate retrieved context to fit within limits
- Prioritize most relevant chunks
- Use summarization for very long documents
Model Selection
We used different models for different tasks:
- Gemini Pro: Main QnA responses
- Smaller models: Query classification and routing
- Embeddings API: Vector generation
Production Challenges
Latency Management
Initial response times were too slow. Solutions:
- Parallel vector search and embedding generation
- Streaming responses for better perceived performance
- Pre-computing embeddings for frequently accessed documents
Error Handling
LLM APIs can fail in various ways. Robust error handling includes:
- Retry logic with exponential backoff
- Fallback to cached responses when available
- Clear error messages for users
- Comprehensive logging for debugging
Monitoring and Observability
We implemented detailed monitoring to track:
- Response quality (user feedback)
- Latency metrics
- API costs per query
- Error rates
- Most common queries
Results and Impact
After deployment, we saw significant improvements:
- 80% reduction in time spent searching for documentation
- Positive feedback from 85% of users
- Average response time under 3 seconds
- API costs within budget constraints
Key Takeaways
- Context is King: The quality of retrieved context directly impacts response quality
- Iterate on Prompts: Prompt engineering is an iterative process based on real user feedback
- Monitor Everything: Comprehensive monitoring is essential for identifying and fixing issues
- Manage Costs: Implement caching and smart context management from day one
- User Feedback Loop: Continuous improvement requires gathering and acting on user feedback
Conclusion
Deploying LLMs in production is challenging but rewarding. The key is to focus on the fundamentals: good data quality, effective prompt engineering, robust error handling, and continuous monitoring. With these in place, LLMs can provide tremendous value to users and organizations.