22 articles
Semantic similarity retrieval sounds solid until your RAG pipeline confidently surfaces the wrong chunks. Learn how cross-encoder rerankers, MMR, and score fusion can fix retrieval quality without rebuilding your entire stack.
Firing one LLM API call per user request sounds fine until traffic picks up and you're hitting rate limits every few minutes. Here's a practical guide to batching requests intelligently so you get throughput without killing response times.
Setting temperature to 0.7 or higher feels like a safe default, but for structured outputs, code generation, and data extraction it quietly corrupts your results. Here's what's actually happening and how to fix it.
Token limit errors don't always throw an exception — sometimes your LLM just quietly drops the middle of your conversation and keeps going. Here's how to detect, prevent, and handle context truncation before it breaks your app.
Your vector search worked great at launch, but results have quietly gotten worse. Embedding drift is the likely culprit — and it's fixable. Here's how to detect it, diagnose the root cause, and restore relevance without starting from scratch.
Bad chunking is the silent killer of RAG pipelines. Your retriever pulls technically correct text but misses the point entirely — here's how to fix it with strategies that preserve meaning across chunk boundaries.
You enabled prompt caching to save money on LLM API calls — but your invoice keeps climbing. Here's why caching often costs more than you expect and how to actually control it.
Most LLM evaluation guides assume you have a labeled dataset to compare against. You usually don't. Here's how to build a practical, automated evaluation pipeline when you're working without a reference answer in sight.
Your RAG pipeline sounds certain, but it's wrong — and that's worse than being uncertain. This guide walks through the most common failure modes, from retrieval misses to prompt leakage, and shows you how to diagnose each one.
Fine-tuning a language model on a consumer GPU sounds like a recipe for out-of-memory crashes. With the right techniques — QLoRA, gradient checkpointing, and careful batch sizing — you can adapt a capable small LLM to your own data on a single 8–16 GB card.