Description
Build Production-Ready AI Applications on Cloud Infrastructure
The LLM Integration Architecture Guide provides comprehensive patterns for integrating large language models into production applications. From Retrieval-Augmented Generation (RAG) architectures to cost-optimized inference pipelines, this guide covers everything your engineering team needs to build reliable, scalable AI-powered applications on cloud infrastructure using commercial and open-source LLMs.
What’s Included
- RAG architecture patterns with document ingestion, chunking strategies, and retrieval optimization
- Vector database comparison and deployment guides for Pinecone, Weaviate, pgvector, and ChromaDB
- Embedding model selection guide with performance benchmarks and cost analysis
- LLM API integration patterns for OpenAI, Anthropic Claude, and self-hosted open-source models
- Cost optimization strategies: caching, prompt compression, model routing, and batch processing
- Production reliability patterns: rate limiting, fallback chains, retry logic, and circuit breakers
- Evaluation and testing framework for LLM application quality with automated test suites
- Cloud deployment architectures on AWS, Azure, and GCP with auto-scaling inference endpoints
Who This Is For
- Software Engineers building LLM-powered features and applications
- Cloud Architects designing infrastructure for AI application workloads
- Technical leads evaluating LLM integration strategies for their product roadmaps
- ML Engineers deploying and optimizing LLM inference at scale
Why Choose Citadel
This guide is built by engineers operating production LLM applications serving real users at scale. Every architecture pattern addresses the practical challenges of LLM integration: latency, cost, reliability, and quality. You get production-tested patterns, not proof-of-concept tutorials, ensuring your AI applications are ready for real-world traffic from launch.
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