Introduction: LLMs can generate harmful, biased, or inappropriate content. They can be manipulated through prompt injection, jailbreaks, and adversarial inputs. Production applications need guardrails—safety mechanisms that validate inputs, moderate content, and filter outputs before they reach users. This guide covers practical guardrail implementations: input validation to catch malicious prompts, content moderation using classifiers and LLM-based […]
Read more →Category: Artificial Intelligence(AI)
Google Agent Development Kit (ADK): Building Your First AI Agent – Part 1 of 5
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 →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 →RESTful AI API Design: Best Practices for LLM APIs
Designing RESTful APIs for LLMs requires careful consideration. After building 30+ LLM APIs, I’ve learned what works. Here’s the complete guide to RESTful AI API design. Figure 1: RESTful AI API Architecture Why LLM APIs Are Different LLM APIs have unique requirements: Async operations: LLM inference can take seconds or minutes Streaming responses: Need to […]
Read more →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 →