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 →Category: Artificial Intelligence(AI)
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 →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 […]
Read more →Introduction to Tokenization
The moment I truly understood tokenization was not when I read about it in a textbook, but when I watched a production NLP pipeline fail catastrophically because of an edge case the tokenizer could not handle. After two decades of building enterprise systems, I have learned that tokenization—the seemingly simple act of breaking text into […]
Read more →Function Calling Deep Dive: Building LLM-Powered Tools and Agents
Introduction: Function calling transforms LLMs from text generators into action-taking agents. Instead of just describing what to do, the model can actually do it—query databases, call APIs, execute code, and interact with external systems. OpenAI’s function calling (now called “tools”) and similar features from Anthropic and others let you define available functions, and the model […]
Read more →