Category: Emerging Technologies

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 […]

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 […]

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, […]

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 […]

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, […]

Introduction: Embedding models convert text into dense vectors that capture semantic meaning. Choosing the right embedding model significantly impacts search quality, retrieval accuracy, and application performance. This guide compares leading embedding models—OpenAI’s text-embedding-3, Cohere’s embed-v3, Voyage AI, and open-source alternatives like BGE and E5. We cover benchmarks, pricing, dimension trade-offs, and practical guidance on selecting […]