How-To & Practical

Fine-tuning is the process of taking an already-trained LLM and training it a bit more on your own examples, so it specializes in…

An LLM API is a way for software to use a language model programmatically — sending it text and getting a response back in code, r…

LLM evaluation is the practice of measuring how good a language model's outputs actually are — whether it's accurate, helpful, saf…

llms.txt is a proposed standard file that websites can publish to tell AI models how to best read and use their content. Placed at…

LLM orchestration is the practice of coordinating multiple LLM calls, tools, and data sources into a single reliable workflow, rat…

An LLM gateway is a single access point that sits between your applications and the various AI models they use, routing requests,…

There isn't one single "best" RAG framework — the three real, actively maintained options worth knowing are LangChain, LlamaIndex,…

Prompt engineering for RAG is writing the instructions that force the model to answer only from the chunks you just retrieved, ins…

RAG evaluation comes down to four questions: did it find the right documents, did the answer actually stick to what those document…

RAG optimization is the set of tuning levers you pull once you already know your retrieval-augmented system is underperforming: be…

Few-shot prompting means giving the AI a few examples of exactly what you want — an input paired with the ideal output — right ins…

Chain-of-thought prompting is asking the model to show its work — to reason step by step out loud — before it gives you a final an…

The fastest way to cut LLM API costs is to stop sending every request to your most expensive model — match the model's size to the…

Prompting wins almost every time you're just starting out — it's free, instant to test, and modern models are good enough to nail…

To build an AI agent, you give a language model a clear goal, a set of tools it can use, and a loop that lets it act, check the re…

Choose a vector database for RAG by matching it to three things: your scale, how much infrastructure you want to run, and the sear…

AI coding assistants work by feeding a large language model two things: your request and the relevant parts of your code. The mode…

Prompt engineering is the practice of writing instructions that get a language model to do what you actually want — reliably, not…

You build a RAG (retrieval-augmented generation) system by connecting an LLM to your own documents so it answers from them instead…

ReAct prompting - short for Reasoning + Acting - is a technique where you get a model to interleave its thinking with real actions…

Tree-of-thought prompting is a technique where a model explores several different lines of reasoning at once, evaluates how promis…

Context engineering is the practice of designing everything a model sees in its context window - instructions, retrieved documents…

The right embedding model is the one that scores highest on your own documents and queries — not the one at the top of a public le…

There's no single best chunk size, but a solid default is roughly 256 to 512 tokens per chunk with about 10 to 20 percent overlap…

Almost always, the problem is retrieval, not the model — the right chunk never made it into the prompt, so the LLM answered from t…

Yes — RAG works with SQL and structured data, but usually not by dumping rows into a vector database. For precise, filterable data…

A RAG system cites its sources by tracking which retrieved chunks were fed to the model and attaching their metadata — document ti…

To write a good prompt, be specific about what you want, give the model the context it needs, state the exact format for the answe…

A prompt template is a reusable prompt with blanks you fill in — the fixed wording stays the same and you swap in the variable par…

A negative prompt tells an AI image model what to leave out. Where your normal prompt describes what you want to see, the negative…

Role prompting (also called persona prompting) is the technique of telling an AI model who to be before you ask your question — "A…

You reduce AI hallucinations with prompting by giving the model less room to guess: ground it in source material you provide, tell…

You write a good AI image prompt by describing it in layers: subject, then descriptors, then style, lighting, composition, and fin…