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AI model output heavily relies on context engineering, not just prompts

By PulseAugur Editorial · [1 sources] ·

The effectiveness of AI models like ChatGPT is heavily influenced by the context provided, which often constitutes over 95% of the input, with the user's prompt being a small fraction. This broader context includes system instructions, conversation history, and retrieved documents, a practice known as context engineering. Developers can significantly improve AI output by understanding and manipulating these contextual layers, which include defining the AI's role, the specific task, injecting relevant knowledge, specifying the output format, and stating constraints. AI

IMPACT Understanding context engineering is crucial for developers to effectively leverage AI models and improve their output.

RANK_REASON The item discusses a concept (context engineering) related to AI models rather than announcing a new release or significant industry event.

Read on dev.to — LLM tag →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

AI model output heavily relies on context engineering, not just prompts

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  1. dev.to — LLM tag TIER_1 English(EN) · Abhijeet Hiwale ·

    Why Your Prompt Is Only 5% of What the Model Sees

    <p>Most developers think they're prompting AI. They're actually injecting a tiny message into a much larger machine — and the machine is mostly running without them.</p> <p>Here's the uncomfortable math: in production AI systems, the user's actual prompt is often less than 5% of …

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