As a developer building with AI, I’ve hit a wall more times than I can count. No matter how polished my prompts were, AI assistants like ChatGPT or Claude would eventually get lost — hallucinating functions that never existed, forgetting variable scopes, or breaking multi-file logic chains.

It took me a while to realize: the issue wasn’t just prompting technique — it was the context model. Or more precisely: the lack of it.

The Truth About Token Limits

Every large language model (LLM) has a context window — a limit on how much information (measured in “tokens”) it can process in one go. Here’s a quick breakdown from real-world data:

Model Max Context Tokens Shared or Separate Input/Output Suggested Static Input Key Limitation Tip GPT-3.5 Turbo ~4,096 Shared ~3,000 Shorten long input; reserve space for output GPT-4 Turbo 128,000 Separate ~100,000 Avoid bloating with irrelevant text Claude 2 100,000 Shared ~80,000 Use summaries instead of full raw data Claude 3 200,000 Shared ~160,000 Prioritize what matters most Gemini 1.5 Pro 1M–2M Separate ~800,000 Even at 1M+, use only what’s relevant Mistral (varied) 32k–128k Shared ~25,000 Break into chunks if needed

You can see that while some models can theoretically digest a million tokens, practical use still demands careful planning. Otherwise, you hit invisible ceilings or get truncated replies.

Context Isn’t Just Capacity — It’s Structure

This is where I kept failing.

Vibe coding is freeing — you don’t plan ahead, you let the idea evolve through the code. But AI hates chaos. Once the project outgrows a few files, the model can’t “see” relationships anymore. You either:

• overfeed it and get cutoff

• underfeed it and get hallucinations

• lose consistency across follow-up prompts

My Solution: A Code Map

After facing this over and over, I built a tool to help myself — and now I’m sharing it. It’s called CodeMap4AI.

What it does:

• Scans your web project

• Analyzes PHP, JS, HTML, CSS, forms, and more

• Outputs a single code_map.json file with a clean overview of your codebase

This structured snapshot gives the AI a full view — without blowing past token limits. You load just what’s needed, and the model works with your code’s logic instead of guessing it.

Practical Tips (Backed by Token Science)

A few universal best practices stand out:

• Use ~75% of token space for static context. Save the rest for dynamic answers.

• Avoid feeding the entire raw code. Summarize, extract relationships, or use code maps (like CodeMap4AI).

• Chunk large projects. Don’t throw 200k tokens at a model in one go. Feed it in stages.

• Use token counting tools. Predict your load before sending it.

• Focus on what matters. Even with million-token windows, relevance trumps volume.

Modern LLMs offer huge capacity — but even they break if you don’t help them think clearly. Structure matters. Context matters. Tools like CodeMap4AI exist to bridge the gap between your evolving project and the AI’s understanding.

Try it, especially if you’ve hit the same walls I have. Less hallucination. More productivity.