Learn how to build production-ready AI agents with Google Agent Development Kit (ADK). This comprehensive tutorial covers architecture fundamentals, setup, and your first search assistant agent with C4 diagrams, code examples, and deployment strategies.
Read more →Category: Artificial Intelligence(AI)
LlamaIndex: The Data Framework for Building Production RAG Applications
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 →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 →Quantization Methods for LLMs: GPTQ, AWQ, and BitsAndBytes
Last year, I needed to run a 13B parameter model on a 16GB GPU. Full precision required 52GB. After testing GPTQ, AWQ, and BitsAndBytes, I reduced memory to 7GB with minimal accuracy loss. After quantizing 30+ models, I’ve learned which method works best for each scenario. Here’s the complete guide to LLM quantization. Figure 1: […]
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 […]
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