Understanding Context Windows in LLMs

Context windows are fundamental to how Large Language Models process and generate text. Understanding their mechanics is crucial for building effective LLM-powered applications.

What is a Context Window?

A context window defines the maximum amount of text an LLM can "see" at once. Modern models like GPT, Claude and Gemini have context windows ranging from 200K to 2M+ tokens.

# Token count varies by model provider
# GPT may equal ~4 characters per token
# Claude may equal ~3.5 characters per token

def estimate_tokens(text, chars_per_token=4):
    return len(text) // chars_per_token

Context Window Challenges

1. Token Limits

When conversations grow long, you need strategies to manage context:

def truncate_context(messages, max_tokens):
    """Keep most recent messages within token limit."""
    total_tokens = 0
    kept_messages = []

    for msg in reversed(messages):
        msg_tokens = estimate_tokens(msg['content'])
        if total_tokens + msg_tokens > max_tokens:
            break
        kept_messages.insert(0, msg)
        total_tokens += msg_tokens

    return kept_messages

2. Lost in the Middle

Research shows LLMs struggle with information in the middle of long contexts. Place important information at the beginning or end.

3. Cost Considerations

More tokens = higher costs. Efficient context management directly impacts your API bills.

Smart Context Strategies

1. Summarization: Compress old messages into summaries
2. Semantic Selection: Use embeddings to select relevant context
3. Hierarchical Context: Maintain different detail levels

Conclusion

Mastering context windows is essential for building production LLM applications. The key is balancing completeness with efficiency.