My approach to “context stacking” for better reasoning in GPT-5

Prompting
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Hey everyone,
I’ve been experimenting with a concept I call context stacking instead of feeding the model a single long instruction block, I build the conversation gradually using multiple system-level context injections.

For example:

  1. Inject domain knowledge (e.g., “you are a travel expert specialized in luxury cruises”).

  2. Inject task format (“respond with structured JSON containing destination, duration, and highlights”).

  3. Inject style context (“use an editorial tone similar to Condé Nast Traveler”).

By layering context this way, GPT-5 seems to maintain coherence and reduce hallucinations, especially when the prompt exceeds ~4K tokens.

Has anyone here tried a similar approach?
Would love to compare results or see if anyone has measured performance differences vs. single-shot prompts.

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There are some situations in which gpt-5 might forget some instructions along many turns.

One approach you can try to solve this, according to the gpt-5 prompting guide, is to use a <persistence> tag around the instructions you require to last longer.

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