Scaling LLM Applications in Production - Blog

[INTRODUCTION]

Building LLM-powered applications is one thing. Scaling them to handle thousands of users with context-aware responses is an entirely different challenge. At Brihaspati and Zoca, we've learned valuable lessons about what works and what doesn't.

[THE CONTEXT PROBLEM]

One of the biggest challenges with LLM applications is managing context effectively. Unlike traditional stateless APIs, LLM interactions often require maintaining conversation history, user preferences, and domain-specific knowledge.

• Context Window Limits — Most LLMs have token limits (4K, 8K, 32K). Smart context pruning and summarization is essential.
• Memory Retrieval — Implemented vector databases (Pinecone/Weaviate) for semantic search of historical conversations.
• Dynamic Prompts — Template-based prompts that adapt based on user behavior and conversation flow.

[OPTIMIZATION STRATEGIES]

At Brihaspati, we improved D7 retention by 20% through these optimization techniques:

class DynamicPromptEngine:
    def __init__(self, context_store):
        self.context_store = context_store

    def build_prompt(self, user_id, query):
        # Retrieve relevant context
        context = self.context_store.get_relevant(
            user_id,
            query,
            limit=5
        )

        # Build dynamic prompt
        prompt = f"""
        Context: {context}
        User Query: {query}

        Generate response considering:
        - User's previous interactions
        - Domain-specific knowledge
        - Conversation coherence
        """

        return prompt

[SCALING INFRASTRUCTURE]

Key infrastructure decisions that allowed us to scale:

• Async Processing — Using Celery + Redis for background LLM calls, preventing API timeouts
• Caching Layer — Redis caching for common queries reduced API costs by 40%
• Rate Limiting — Token bucket algorithm to manage API rate limits across multiple users
• Monitoring — Custom metrics for token usage, latency, and quality scores

[LESSONS LEARNED]

After processing 100K+ daily requests, here's what we learned:

1. Start with smaller models and scale up only when necessary
2. Implement robust fallback mechanisms for API failures
3. Monitor token usage obsessively - costs can spiral quickly
4. User feedback loops are essential for prompt improvement
5. Context-aware responses > Generic responses (20% better retention)

[CONCLUSION]

Scaling LLM applications requires a different mindset from traditional backend development. Focus on context management, optimize for costs, and always measure quality metrics alongside performance metrics.

The field is evolving rapidly. What works today might be outdated in six months. Stay curious, experiment, and share learnings with the community.