22 articles
Hybrid search promises the best of both worlds — exact keyword matching and semantic understanding — but the way you combine scores matters enormously. Here's what goes wrong and how to fix it.
Quantizing your embeddings can slash memory usage by 4x or more — but push it too far and your similarity search starts returning garbage. Here's how to find the line before you cross it in production.
LLMs consistently over-attend to the very first tokens in a prompt, even when those tokens carry no semantic meaning. Understanding why this happens can help you write better prompts and debug surprising model behavior.
Switched embedding models mid-project and suddenly your semantic search returns garbage? The problem isn't your queries or your data — it's a fundamental incompatibility baked into how vector stores work. Here's what's happening and how to fix it.
Semantic caching helps reduce LLM costs and latency by reusing responses for semantically similar queries. However, many AI teams discover that seemingly identical questions still bypass their cache. This article explores how semantic caches work, the most common causes of unexpected cache misses
You build an LLM agent that handles multi-step tasks beautifully in demos, then it falls apart after a few exchanges in production. This is memory collapse — and understanding why it happens is the first step to fixing it.
LLMs don't hallucinate randomly — they confabulate more on specific query types like obscure facts, recent events, and multi-step reasoning. Understanding these hotspots helps you build better prompts and catch errors before they cost you.
Stuffing every message into your LLM's context window feels safe, but it quietly degrades response quality. Learn why long histories confuse models and how to trim context without losing what matters.
Your RAG pipeline works fine in staging, then randomly stalls for 3–8 seconds in production. Before you blame the vector database, there are five more likely culprits worth checking first — and most of them are fixable in an afternoon.
Your LLM agent keeps calling functions with null values, wrong types, or missing required fields. Here's why it happens and exactly how to fix it — from schema design to retry logic and argument validation.
You've carefully crafted a system prompt to shape your AI app's behavior — but users may be able to extract every word of it. Here's how prompt leakage works, why it's harder to prevent than you'd expect, and what you can actually do about it.
JSON mode is supposed to guarantee valid output from LLMs, but you've probably already hit cases where the data is structurally broken, semantically wrong, or silently truncated. Here's what's actually going wrong and how to fix it.