intermediate
7 min read
What Is Retrieval-Augmented Generation (RAG)?
Most people assume an LLM knows everything. Ask it about your company's return policy, and it will confidently answer—even if that policy changed last week…
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RAG and Agents
Step into more advanced LLM applications: retrieval-augmented generation (RAG) and building simple AI agents.
Tutorials
Articles in RAG and Agents
Follow these lessons in order, or jump directly to the topic you need.
intermediate
7 min read
RAG vs Plain LLMs
Most people assume RAG is a different kind of model—a smarter cousin of the plain LLM. It's not. RAG doesn't change the model at all. It changes what goes…
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intermediate
10 min read
Simple RAG Tutorial
Reading about RAG is easy. Building one is where the real learning happens—because the first pipeline you run will almost certainly fail in ways no diagram…
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intermediate
9 min read
What Are LLM Agents?
An LLM agent is not a smarter chatbot. It is a goal-driven system that uses a language model as its brain, then loops through action and observation until…
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intermediate
10 min read
Build a Simple LLM Agent
Most beginners expect an agent to be a special kind of model—something with built-in magic that can browse the web, run code, and get things done. Then…
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intermediate
13 min read
RAG and Agents Practice Exercises
You can explain RAG. You can sketch an agent loop. But when a real question lands on your desk, do you know whether to retrieve, route, or just answer?…
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intermediate
12 min read
The RAG Pipeline Explained: From Documents to Grounded Answers
You know RAG retrieves documents and feeds them to a language model. But if someone asked you what actually happens between a source PDF and a grounded…
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intermediate
9 min read
What Are Embeddings? How Meaning Becomes Searchable
You search your company's help docs for "how do I reset my password?" and get nothing useful. You know the answer is in there—you've read the page…
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intermediate
10 min read
RAG Chunking Explained: How to Split Documents Without Losing Meaning
You ask your RAG system a question. It retrieves a passage that is technically relevant and completely useless—a sentence fragment that starts mid-thought,…
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intermediate
8 min read
LLM Agents vs Workflows: When Should You Use Each?
You have a task that needs an LLM. The temptation is to reach for an agent—something autonomous, something that "figures it out." But most of the time, a…
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intermediate
12 min read
LLM Tool Calling Explained: From Model Choice to Real-World Action
When people first hear that an LLM can "use tools," they usually picture the model reaching out and doing something itself—searching the web, running code,…
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intermediate
11 min read
Common LLM Agent Failures: Loops, Wrong Tools, and Lost Context
Your agent runs. It calls tools. It produces output. And yet, something is wrong—or it never stops running at all. The frustrating part is that you can't…
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intermediate
10 min read
Vector Databases Explained: Where Semantic Search Finds Evidence
A vector database is not a fancy search box or a generic "database for AI." It is the retrieval engine that decides which evidence earns a seat on the…
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intermediate
10 min read
RAG Query Rewriting Explained: Helping Retrieval Ask the Right Question
Your knowledge base has the answer. You know it does. You wrote the documents, chunked them carefully, embedded them, and verified the chunks look right.…
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intermediate
11 min read
RAG Reranking Explained: Choosing the Best Evidence After Retrieval
Your retriever returns ten chunks. They all look topically related to your question. None of them actually answer it. The LLM dutifully composes a response…
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intermediate
8 min read
RAG Citations and Grounded Answers: Linking Claims to Evidence
A citation is not proof. It is a pointer — and a pointer is only as trustworthy as the instruction that produced it and the check that verifies it.
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intermediate
12 min read
LLM Agent Memory Explained: Context, Retrieval, and Stored State
You build an agent. You chat with it for a few turns. It helps you draft a plan, picks a tool, runs it, reports back. Then you ask: "What was the second…
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intermediate
11 min read
Agent Loops, Budgets, and Stopping Rules: How to Bound Autonomy
Here's the scene I've watched play out more times than I can count: someone builds their first agent, gives it a task, and watches it loop. It calls a…
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intermediate
9 min read
Structured Output vs Tool Calling: Data Returned or Action Requested?
Structured output and tool calling look almost identical from the outside. Both accept a JSON schema. Both return clean key-value pairs instead of…
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