Category: Technology Engineering

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… Continue reading

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… Continue reading

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… Continue reading

Introduction: Embeddings are the foundation of semantic search, RAG systems, and similarity-based applications. Choosing the right embedding model and strategy significantly impacts retrieval quality, latency, and cost. Different models excel at different tasks—some optimize for semantic similarity, others for retrieval, and some for specific domains. This guide covers practical embedding strategies: model selection based on… Continue reading

Introduction: Getting LLMs to return structured data instead of free-form text is essential for building reliable applications. Whether you need JSON for API responses, typed objects for downstream processing, or specific formats for data extraction, structured output techniques ensure consistency and parseability. This guide covers the major approaches: JSON mode, function calling, the Instructor library,… Continue reading

Introduction: LLM applications are notoriously hard to test. Outputs are non-deterministic, “correct” is often subjective, and traditional unit tests don’t apply. Yet shipping untested LLM features is risky—prompt changes can break functionality, model updates can degrade quality, and edge cases can embarrass your product. This guide covers practical testing strategies: building evaluation datasets, implementing automated… Continue reading

Introduction: Waiting 10-30 seconds for an LLM response feels like an eternity. Streaming changes everything—users see tokens appear in real-time, creating the illusion of instant response even when generation takes just as long. Beyond UX, streaming enables early termination (stop generating when you have enough), progressive processing (start working with partial responses), and better error… Continue reading

Introduction: Prompt injection is the most significant security vulnerability in LLM applications. Attackers craft inputs that manipulate the model into ignoring instructions, leaking system prompts, or performing unauthorized actions. Unlike traditional injection attacks, prompt injection exploits the model’s inability to distinguish between instructions and data. This guide covers practical defense strategies: input sanitization, injection detection,… Continue reading

Introduction: Getting reliable, structured data from LLMs is one of the most practical challenges in building AI applications. Whether you’re extracting entities from text, generating API parameters, or building data pipelines, you need JSON that actually parses and validates against your schema. This guide covers the evolution of structured output techniques—from prompt engineering hacks to… Continue reading

Introduction: LLM inference can be slow and expensive, especially at scale. Optimizing inference is crucial for production applications where latency and cost directly impact user experience and business viability. This guide covers practical optimization techniques: semantic caching to avoid redundant API calls, request batching for throughput, streaming for perceived latency, model quantization for self-hosted models,… Continue reading