LLM & RAG Systems

Prompt engineering isn't a dark art. It's the skill of communicating clearly with a system that takes you literally. Here's how to start.

Your LLM will generate something you did not expect. The guardrail system determines whether that something reaches your users or gets caught at the gate.

Your RAG pipeline's biggest weakness is probably not the LLM — it's the retrieval step. If you're pulling the wrong documents, no amount of prompt engineering will save you.

A no-hype explanation of what large language models actually are, how they work, and what they can and cannot do. Covers tokens, training, inference, and real-world model comparisons.

Your RAG system is only as good as its retrieval. And retrieval quality starts with how you embed your documents — the model, the chunk size, and the metadata that makes search precise.

You cannot improve what you cannot measure — and measuring LLM output quality is harder than any metric you've dealt with before. Here's how to build evaluation systems that actually tell you if your LLM application is getting better.

AI coding assistants are remarkably useful and reliably wrong in specific, predictable ways. Knowing the failure patterns turns you from a passive consumer of AI-generated code into an effective reviewer.

There's a spectrum between 'call the API and return the response' and 'build an autonomous agent.' Here are the architectural patterns for LLM applications, when to use each, and when simpler is better.

LLMs hallucinate when they don't have the right context. RAG fixes this by retrieving real documents before generating answers. Here's the architecture behind it.

Understanding what large language models can't do prevents disappointment, misuse, and bad decisions.

How to write prompts that get useful results from AI. Practical techniques, not magic incantations.

Running AI on your machine vs using a cloud service. The real tradeoffs in cost, privacy, quality, and control.

The demo was free. Production is not. Here's what it actually costs to run LLMs at scale, where the latency hides, and the architectural decisions that determine whether your LLM feature is viable.

Your RAG prototype works. Now it needs to handle real traffic, real costs, and real failure modes. Here's what changes when retrieval-augmented generation meets production.