From tokenmaxxing to context engineering: Why enterprise AI needs better context, not bigger prompts

Enterprise AI used to focus on feeding models as much data as possible, believing bigger inputs meant better results. However, the real key is not how much data a model consumes, but what it actually needs to know. Tokenmaxxing, a trend where companies measured AI productivity by token consumption, proved problematic because it rewarded quantity over quality.

Instead of focusing on massive prompts, organizations now must determine what specific context a model should have at each stage of its reasoning process. This is where context engineering comes in - deciding exactly which tasks, tools, retrieved information, and data to include or exclude. Retrieval becomes critical, as the model must be grounded in trusted internal knowledge without being overwhelmed by it.

Relevant, high-quality data allows autonomous AI to act safely without error propagation. Elastic, for example, approaches context engineering through three layers: data proximity, retrieval precision using a hybrid search method, and execution grounding to load only necessary agent capabilities. This single platform can run on-premises or in air-gapped environments, crucial for businesses with data localisation requirements like India.

The biggest mistake is assuming connecting a model to data equals grounding it in enterprise context - relevance must be ensured. Smaller, well-grounded models outperform larger ones with fragmented context. Ultimately, model selection matters less than the quality of context an AI has to work with. Enterprises must measure AI success by how well a model reasons over relevant data, rather than token usage.