Results for "context window"
Article · LLM foundations
What is a context window in an LLM?
The context window is the maximum number of tokens a language model can consider at once. Learn what fills it, what happens when it overflows, and how to budget it.
Tool · AI and LLMs
Token counter
Count tokens with a real tokenizer and see how much of a context window your text fills.
Article · LLM foundations
Why long context windows still miss things
A million-token context window does not mean a model uses every token well. Learn about the lost-in-the-middle effect, attention cost, and how to test it yourself.
Learning path · Learning path: AI
How language models read text
Tokens, context windows and embeddings: what a model actually receives.
Lab · Retrieval-augmented generation
Build a tiny RAG system in Python
Build a working retrieval-augmented generation pipeline in about 60 lines of Python: chunk notes, retrieve with TF-IDF, build a grounded prompt, and inspect every step.
Article · Retrieval-augmented generation
How to chunk documents for RAG
Chunking decides what your RAG system can retrieve. Compare fixed-size, recursive, heading-aware and semantic chunking, and learn how to pick size and overlap.
Article · LLM foundations
What is a token in an LLM?
A token is the unit of text a language model reads and writes. Learn how tokenizers split text, why token counts differ by language, and why it matters for cost.
Article · Retrieval-augmented generation
How to evaluate a RAG system: retrieval and answer quality
Measure a RAG system in two layers: did retrieval find the right passages, and is the answer faithful to them? Learn recall@k, MRR, faithfulness and a practical test set.
Project · Project
An AI tutor that shows its retrieval
The tutor on this site is a small, inspectable RAG system: heading-aware chunking, BM25 or hybrid retrieval, grounded prompts and visible sources.
Article · Retrieval-augmented generation
What is retrieval-augmented generation (RAG)?
RAG makes a language model answer from your own documents by retrieving relevant passages first. Learn the pipeline step by step, and when RAG is the right choice.
Article · LLM foundations
What are embeddings? Meaning as numbers, explained
Embeddings turn text into lists of numbers so that similar meanings land close together. Learn how they work, how similarity is measured, and where they fail.
Article · LLM foundations
What is an LLM (Large Language Model)? A Complete Beginner-to-Builder Guide
A beginner-to-builder guide to large language models: next-token prediction, the transformer and attention, how models are trained, and how to start a career.
Lab · Networking
Watch a TCP handshake in Wireshark
Capture your own web traffic in Wireshark, find the TCP three-way handshake, compare it with a UDP DNS lookup, and learn the display filters analysts use every day.
Article · Networking
TCP vs UDP: differences, handshakes and when each is used
TCP gives reliable, ordered delivery after a handshake; UDP sends independent datagrams. Compare headers, use cases, ports and how both look in Wireshark.