Run ten real student questions through your agent before going live. Compare the answers to what you'd actually say. If more than two are off-base, your context needs work — not a different AI tool.
A context leak happens when an AI agent reveals its system prompt or private instructions to a user who asks the right question. This can expose your business rules, pricing logic, or confidential configurations.
Some AI agents can search the web in real time, but most work from a fixed knowledge base with a training cutoff date. Whether your agent has live web access depends on the tool and how it's configured.
You can upload files directly to tools like Claude or ChatGPT, or connect a knowledge base so your agent can search your documents on demand. The best approach depends on how often your content changes.
A system prompt is the behind-the-scenes instruction you write to configure the agent's behavior. A user prompt is what the student or person actually types when they interact with the agent.
Same underlying model, wildly different behavior — the difference almost always comes down to context: the instructions, examples, and constraints each agent was given, not the training data itself.
Treat your agent's context like a living document. When your offer, pricing, schedule, or policies change, update the context file and re-test the agent before students interact with it again.
Your campus AI agent needs four things: who it is, who your students are, what your course covers, and what it should do when it doesn't know the answer.
Put your most important instructions first and last in the context. AI agents pay more attention to what appears at the beginning and end of their instructions than what's buried in the middle.
A context limit is the maximum amount of text an AI agent can hold in its working memory at one time. When an agent hits that limit, it loses access to earlier parts of the conversation.