Introduction: LLM API calls are slow—often 1-10 seconds per request. Sequential processing kills throughput. Async patterns let you process multiple requests concurrently, dramatically improving performance for batch operations, parallel tool calls, and high-traffic applications. This guide covers async LLM patterns in Python: using asyncio with OpenAI and Anthropic clients, managing concurrency with semaphores, implementing retry […]
Read more →Author: Nithin Mohan TK
Hello Namaste! 👋🏻
I'm an accomplished Enterprise Solution Architect & Engineering Leader with 20+ years of progressive experience architecting, building, and scaling transformative enterprise solutions.
⚜️ Solution Architect / AI & Cloud 📌 Galway, Ireland
Function Calling Patterns: Tool Schemas, Execution Pipelines, and Agent Loops
Introduction: Function calling transforms LLMs from text generators into capable agents that can interact with external systems. By defining tools with clear schemas, models can decide when to call functions, extract parameters from natural language, and incorporate results into responses. This guide covers practical function calling patterns: defining tool schemas, handling multiple tool calls, implementing […]
Read more →Fine-Tuning LLMs: From Data Preparation to Production Deployment
Introduction: Fine-tuning transforms a general-purpose LLM into a specialized model tailored to your domain, style, or task. While prompt engineering can get you far, fine-tuning offers consistent behavior, reduced token usage, and capabilities that prompting alone cannot achieve. This guide covers the complete fine-tuning workflow—from data preparation to deployment—using both cloud APIs (OpenAI, Together AI) […]
Read more →Advanced RAG Patterns: Beyond Basic Retrieval
Six months ago, I thought RAG was simple: retrieve chunks, send to LLM, done. Then I built a system that needed to answer questions about 50,000 technical documents. Basic retrieval failed spectacularly. That’s when I discovered advanced RAG patterns—techniques that transform RAG from a prototype into a production system. ” alt=”Advanced RAG Patterns” style=”max-width: 100%; […]
Read more →Vector Search Optimization: HNSW, IVF, and Hybrid Retrieval
Introduction: Vector search powers semantic retrieval in RAG systems, recommendation engines, and similarity search applications. But naive vector search doesn’t scale—searching millions of vectors with brute force is too slow for production. This guide covers optimization techniques: HNSW indexes for fast approximate search, IVF partitioning for large datasets, product quantization for memory efficiency, hybrid search […]
Read more →Testing LLM Applications: Unit Tests, Integration Tests, and Evaluation
Introduction: Testing LLM applications presents unique challenges compared to traditional software. Outputs are non-deterministic, quality is subjective, and the same input can produce different but equally valid responses. This guide covers practical testing strategies: unit testing with mocked LLM responses, integration testing with real API calls, evaluation frameworks for quality assessment, and regression testing to […]
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