Large Language Models (LLMs) exhibit measurable degradation in instruction-following performance across extended multi-turn conversations, a phenomenon driven by compounding errors, contextual drift, and architectural constraints such as the finite context window. As conversational history grows, the model’s attention diffuses across an increasingly noisy context, causing it to over-rely on its own prior outputs and deviate from foundational user instructions. This paper proposes Contextual Anchoring, a lightweight prompting methodology that addresses these failure modes without external tooling or model fine-tuning. The technique involves pre-defining core instructions and output formats using symbolic tags at conversation initialisation, then explicitly referencing these tags in subsequent prompts to re-ground the model’s attention on foundational requirements. We evaluate this approach empirically through a structured experiment involving ten professional AI trainers, two frontier models (Gemini 3.1 Pro and ChatGPT 5.4 Thinking), and an 80-turn data labelling task. Results indicate that baseline instruction-following rates (IFR) averaged 73% for Gemini 3.1 Pro and 68% for ChatGPT 5.4 Thinking across all 80 turns. With Contextual Anchoring applied at each turn, IFR improved to 96% and 93% respectively, representing gains of 23 and 25 percentage points. These findings suggest that Contextual Anchoring is a practical and effective technique for improving LLM reliability in long-horizon, instruction-intensive tasks.