Introduction: LlamaIndex (formerly GPT Index) is the leading data framework for building LLM applications over your private data. While LangChain focuses on chains and agents, LlamaIndex specializes in data ingestion, indexing, and retrieval—the core components of Retrieval Augmented Generation (RAG). With over 160 data connectors through LlamaHub, sophisticated indexing strategies, and production-ready query engines, LlamaIndex […]
Read more →Category: Technology Engineering
Technology Engineering
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 →Advanced RAG Patterns: From Naive Retrieval to Production-Grade Systems (Part 1 of 2)
Introduction: Retrieval-Augmented Generation (RAG) has become the go-to architecture for building LLM applications that need access to private or current information. By retrieving relevant documents and including them in the prompt, RAG grounds LLM responses in factual content, reducing hallucinations and enabling knowledge that wasn’t in the training data. But naive RAG implementations often disappoint—the […]
Read more →LLM Security: Defense Patterns for Production Applications (Part 2 of 2)
Introduction: LLM applications face unique security challenges—prompt injection, data leakage, jailbreaking, and harmful content generation. Traditional security measures don’t address these AI-specific threats. This guide covers defensive techniques for production LLM systems: input sanitization, prompt injection detection, output filtering, rate limiting, content moderation, and audit logging. These patterns help you build LLM applications that are […]
Read more →MLOps Best Practices: Building Production Machine Learning Pipelines That Scale
Master MLOps practices for production machine learning systems. Learn data versioning, experiment tracking with MLflow, CI/CD for ML, model registry governance, and monitoring strategies for AWS, Azure, and GCP.
Read more →LLM Fine-Tuning Techniques: From LoRA to Full Parameter Training
Introduction: Fine-tuning transforms general-purpose LLMs into specialized models that excel at your specific tasks. While prompting can get you far, fine-tuning unlocks capabilities that prompting alone cannot achieve: consistent output formats, domain-specific knowledge, reduced latency from shorter prompts, and behavior that would require extensive few-shot examples. This guide covers the practical aspects of LLM fine-tuning: […]
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