8 articles
What Is RAG? A Practical Guide to Retrieval-Augmented GenerationWhat RAG is, when to use it, and how the retrieval pipeline actually works — chunking, embeddings, hybrid search, reranking, and evaluation, end to end.
RAG Isn't Dead — But Your Chunking Strategy Probably IsMost failing RAG systems don't have a model problem, they have a retrieval problem. Here's how chunking, embeddings, and reranking actually decide whether your answers are any good.
Guardrails: Validate LLM Output Before It Reaches Your UsersAn LLM will confidently return malformed JSON, leaked prompts, or unsafe content. Treat its output as untrusted input and validate it like you would a form submission.
Cutting LLM Cost Without Cutting Quality: Model Routing + CachingMost LLM bills are bloated by sending every request to your biggest model. Routing and caching cut cost dramatically while holding quality steady.
Begging a model for JSON and hoping it parses is a bug waiting to happen. Schema-constrained structured outputs make it a guarantee. Here's how.
Agents call tools in a loop. Without the right guardrails that loop burns money, hangs, or repeats itself forever. Here's how to keep it bounded.
You wouldn't ship code without tests. Stop shipping prompts without evals. A practical guide to building evaluation suites for LLM features.
Prompt caching can cut latency and cost on repeated context by an order of magnitude. Here's how it works and why most teams leave it on the table.
