Context Window LLM: Everything You Need to Know

Large Language Models (LLMs) like GPT, Claude, and LLaMA are revolutionizing how we interact with artificial intelligence. But there’s one concept that defines what these models can and cannot do: the Context Window LLM.

In simple terms, a context window is the maximum amount of text (measured in tokens) that an LLM can process in a single interaction. This limitation directly impacts how much the model can “remember” in a conversation, analyze in a document, or generate in a single response.

As Andrew Ng once said, “Data is the new oil, but context is the refinery.” Without understanding the limits of context windows, even the most powerful LLMs can produce inaccurate or incomplete results.

What Does Context Window Mean in LLMs?

Every word, number, or symbol given to an LLM is broken into tokens. The model can only handle a fixed number of tokens at once, known as the context window.

Example:

• A 4,000-token window ≈ 3,000 words
• A 32,000-token window ≈ 24,000 words
• Anthropic’s Claude 2 can handle 100,000 tokens (~75,000 words)

So when you ask an LLM a question, everything—your prompt, previous conversation history, and system instructions—counts toward this window.

Why Does Context Window Matters?

Context Window Sizes in Popular LLMs

from transformers import GPT2TokenizerFast

tokenizer = GPT2TokenizerFast.from_pretrained("gpt2")

def chunk_text(text, max_tokens=3500):
    tokens = tokenizer.encode(text)
    for i in range(0, len(tokens), max_tokens):
        yield tokenizer.decode(tokens[i:i+max_tokens])

# Pseudocode for semantic search with RAG
query_vector = embed("What were Q2 sales?")
results = vector_db.search(query_vector, top_k=3)
context = "\n".join([r['text'] for r in results])
response = llm(f"Answer using:\n{context}")