Topic
Context Window & Token Limit Debugging
12 posts on context window & token limit debugging — part of developer tools on the n4n AI blog.
Why your prompt fits locally but fails against the API
Why your prompt fits locally but fails against the API: tokenizer mismatches, hidden overhead, and context window debugging for engineers.
Why token counts differ between OpenAI and Anthropic
Understand why token count openai vs anthropic differs, how tokenization algorithms diverge, and what it means for context limits, cost, and caching.
What happens when you exceed a model's context window
When you exceed model context window, the API rejects or truncates input. Learn exactly what happens, error shapes, and how to handle token limits in production.
Truncating conversation history without losing context
Practical methods to truncate conversation history context while preserving meaning. Includes runnable Python examples and verification tips.
Strategies for summarizing history to save context tokens
Practical steps to summarize chat history and save context tokens in production LLM apps, with code for token budgeting, compression loops, and verification.
Managing sliding window context in long chat sessions
Practical guide to building a sliding window context chat session: token counting, summarization policy, tool-call pitfalls, and provider fallback.
How to chunk long documents for context window limits
Step-by-step tutorial on how to chunk long documents for context window limits in Python, with token-aware splitting and runnable code for RAG.
Debugging truncated responses from max_tokens limits
Step-by-step guide to debugging a truncated response max_tokens limit in production LLM apps: reproduce, read finish_reason, size tokens, and retry.
Debugging token count mismatches between tokenizers
Step-by-step guide to debugging token count mismatch tokenizer issues across LLM providers, with runnable code and verification checks.
Debugging context length exceeded errors
Practical steps to diagnose and fix context length exceeded errors in LLM apps: token counting, prompt trimming, model limits, and runtime fallback.
Counting tokens accurately before hitting context limits
Learn how to count tokens accurately context limit before sending LLM requests, with step-by-step code to avoid truncation and rate-limit errors.
Context window sizes compared: GPT-5, Claude, Gemini
Compare context window sizes gpt-5 claude gemini across capabilities, cost, latency, and limits. A pragmatic engineering guide to choosing the right model.
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