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
Give LLM applications evidence and actions with embeddings, retrieval, reranking, citations, tools, agents, memory, and controlled loops.
Tutorials
Articles in RAG and Agents
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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
10 min read
RAG Chunking Math: Calculate Chunk Counts, Overlap, and Token Use
A 1,500-token document with 512-token chunks and 15% overlap quietly becomes four chunks, and the same paragraph now lives in two of them. Nobody chose…
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intermediate
11 min read
Test RAG Chunking Strategies on a Small Document Set
Retrieval misses rarely announce their cause. The answer was in the document, the query was reasonable, and the model still returned something useless. The…
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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
8 min read
LLM Tool-Calling Control Flow: States, Results, and Failure Boundaries
That gap is not bad luck. It is a missing model. Most beginners picture tool calling as a straight line: ask, call, answer. That picture is accurate for…
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intermediate
10 min read
Practice Checking LLM Tool Results Against the Application Contract
A tool call returns status: "ok". The pipeline moves on. Nobody notices that the result answers a slightly different question than the one the model asked.
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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
9 min read
The Candidate-Recall Ceiling in RAG Reranking
You swap in a stronger reranker. The top three results look sharper, the ordering feels smarter, and the demo lands. Then you measure recall and it barely…
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intermediate
11 min read
Evaluate a RAG Reranking Stage Against Relevance Judgments
The reranker "feels" better. The top result looks more relevant than it did before. And yet you cannot say whether the stage earned its latency, because…
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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
12 min read
Agent Loop Budgets: Model Iterations, Cost, and Stopping Risk
A loop that usually finishes in three steps can still spend most of its money on the runs that don't.
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intermediate
8 min read
Practice Applying Agent Stopping Rules to Loop Traces
You can recite the four dispositions — continue, stop, retry, handoff — and still stare at a raw trace with no idea which one applies. That gap is the…
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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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intermediate
8 min read
Build and Debug a Small RAG Pipeline
A clean pipeline that answers confidently and wrongly is not a mystery. It is a trace you have not read yet.
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intermediate
9 min read
Build and Inspect a Bounded Tool-Using Agent
The agent worked in the demo. Then you ran it on your own task, and it called a tool you never expected — or kept calling tools after the answer was…
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beginner
9 min read
Embedding Similarity Math: Dot Products, Cosine, and Retrieval Ranking
A retrieval system returns a top result with a similarity score of 0.83. Most people read that number as "83% relevant" or "83% likely to be correct." It…
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intermediate
11 min read
RAG Retrieval Metrics: Precision, Recall, Reciprocal Rank, and NDCG
Two retrievers return the same relevant chunk. One puts it at rank 1; the other buries it at rank 4. A single "accuracy" number calls them identical. The…
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intermediate
12 min read
Practice Auditing Evidence for RAG Answer Claims
A citation is a pointer, not a proof. This drill teaches you to stop trusting the pointer and start reading the span.
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intermediate
10 min read
Practice Auditing Agent Memory for Stale or Conflicting State
Your agent answered confidently. It was also wrong, and the memory record it trusted was three sessions old.
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intermediate
10 min read
Test RAG Query Rewrites Against a Fixed Retrieval Task
You add a rewrite step. The demo answers the question. You feel clever. Then someone asks a slightly different question and the whole thing falls apart,…
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intermediate
10 min read
Model the Cost and Stale-State Risk of Agent Memory
A team sets memory to refresh every six hours to save money. Four hours later, an agent quotes a price, policy, or preference that has already changed. The…
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intermediate
11 min read
Test Metadata Filters in a Small Vector Search
A filter can be correct and still delete your best evidence. Here is how to watch it happen in twenty lines of NumPy.
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intermediate
10 min read
Approximate Nearest-Neighbor Search: Recall, Memory, and Latency Tradeoffs
A retrieval system answers in four milliseconds over a million vectors. Your first instinct is that the index must be computing similarity faster than…
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intermediate
9 min read
Practice Triage: Find the Likely Cause of an LLM Agent Failure
The run failed. The trace is four hundred lines long. Your instinct says the model is dumb — and that instinct is almost always the first thing to throw…
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intermediate
10 min read
Hybrid Retrieval Math: Combining Lexical and Vector Search
Two retrievers return two different top-5 lists for the same query. You merge them, and the merged order looks like it was decided by a coin flip. It…
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intermediate
11 min read
Measure Citation Coverage and Support in RAG Answers
A citation marker is a promise. The sentence says this passage backs me up. Most RAG evaluations never check whether the promise holds — they check whether…
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intermediate
10 min read
Practice Choosing an Agent Memory Strategy for Each Task
The agent remembered everything and still gave the wrong answer. That is the failure this drill is built to prevent.
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intermediate
13 min read
Practice Comparing Lexical, Vector, and Hybrid Retrieval
Most teams add hybrid retrieval because a vendor page promised better accuracy, then never check whether it helped their own corpus. This exercise forces…
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intermediate
9 min read
Practice Propagating RAG Document Corrections and Deletions
A query returns a sentence the source no longer contains. The team blames the model. The model is innocent — the index is still holding a chunk that was…
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intermediate
9 min read
Practice Resolving Conflicting Sources in a RAG Answer
Retrieval worked. Three chunks came back, all relevant, all cited. Two of them disagree. Now what?
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intermediate
12 min read
Model the Cost of Choosing an Agent or Fixed Workflow
Most teams pick agents or workflows by vibe. The demo looked smart, the diagram looked clean, and nobody wrote down what the choice would cost per run.…
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