Contextual AI Training

Contextual Training

Contextual Training teaches your AI Agents how to respond accurately by learning from real conversations. Instead of relying on fixed rules, your agents learn from actual interactions, including customer intent, tone, product details, and the steps your team takes to resolve an issue. This gives them the nuance needed to handle multi-step resolutions and stay aligned with your brand standards.

How Contextual Training Works

When you review an interaction and correct the agent’s output, Quack analyzes the full conversation and treats your correction as a learning example. That context is added to the agent’s training loop so it can apply the same logic automatically in future conversations.

What You Can Teach

You can use contextual training to teach agents how to follow your company policies, how to resolve recurring issues step-by-step, how to match your brand tone, how to escalate correctly, and how to avoid common mistakes. Each correction makes the agent more accurate and consistent.

How to Use Contextual Training: Step by Step

  1. Open a ticket or conversation where an AI Agent was involved in the response.

  2. Review the agent’s suggested reply or action and compare it to how your team should handle this type of case.

  3. If something is missing or incorrect, update the reply or resolution steps to reflect the ideal behavior: correct the wording, the steps taken, or the decision made.

  4. Submit or save your changes so Quack can treat this as a training example for the agent.

  5. Repeat this process on additional relevant conversations to give the agent multiple examples of the same pattern.

  6. Over time, monitor similar cases to confirm that the agent is applying the improved behavior consistently.

Where You’ll See the Impact

You’ll notice the results of contextual training across the platform. Agent suggestions in Copilot become smarter, automated resolutions improve in accuracy, enrichment values become more reliable, and insights in Explore reflect better classification and understanding.