Category: Technology Engineering

Introduction: Getting LLMs to output valid JSON, XML, or other structured formats is surprisingly difficult. Models hallucinate extra fields, forget closing brackets, and produce malformed output that breaks downstream systems. Prompt engineering helps but doesn’t guarantee valid output. This guide covers techniques for reliable structured generation: using native JSON mode and structured outputs, constrained decoding […]

Introduction: LLM API calls are expensive and slow. A single GPT-4 request can cost $0.03-0.12 and take 2-10 seconds. When users ask similar questions repeatedly, you’re paying for the same computation over and over. Caching solves this by storing responses and returning them instantly for matching requests. But LLM caching is harder than traditional caching—users […]

Introduction: Choosing the right embedding model is critical for RAG systems, semantic search, and similarity applications. The wrong choice leads to poor retrieval quality, high costs, or unacceptable latency. OpenAI’s text-embedding-3-small is cheap and fast but may miss nuanced similarities. Cohere’s embed-v3 excels at multilingual content. Open-source models like BGE and E5 offer privacy and […]

Introduction: Chain-of-thought (CoT) prompting dramatically improves LLM performance on complex reasoning tasks. Instead of asking for a direct answer, you prompt the model to show its reasoning step by step. This simple technique can boost accuracy on math problems from 17% to 78%, and similar gains appear across logical reasoning, code generation, and multi-step analysis. […]

Introduction: Tool use transforms LLMs from text generators into capable agents that can search the web, query databases, execute code, and interact with APIs. But implementing tool use well is tricky—models hallucinate tool calls, pass invalid arguments, and struggle with multi-step tool chains. The difference between a demo and production system lies in robust tool […]

Introduction: Retrieval Augmented Generation (RAG) grounds LLM responses in your actual data, reducing hallucinations and enabling knowledge that wasn’t in the training set. But naive RAG—embed documents, retrieve top-k, stuff into prompt—often disappoints. Retrieval misses relevant documents, context windows overflow, and the model ignores important information buried in long contexts. This guide covers advanced RAG […]