Beyond the Chatbot: Engineering Context-Aware LLM Applications

Almost every organization now has a chatbot prototype. It answers a few questions impressively in a demo, and then it meets real users — who ask about their own order, their own policy, their own account — and the illusion fades. The model never knew any of that. It was never given the context, and it was never connected to the systems where that context lives.

That gap is the real work of building with large language models. The interface may be a chat window, a voice assistant or an invisible step inside a workflow; what decides whether it is useful is the engineering around the model. At Aviox Blogs we break that work into four disciplines.

1. Start with intents, not prompts

The first instinct on an LLM project is to open a playground and start writing prompts. The better first step is quieter: understand the use cases, the intents behind them, and the workflows they sit inside. Which questions do people actually ask? What do they need to happen next? Where does an answer stop being enough and an action become necessary?

A few questions are worth answering before a single prompt is written:

• Which tasks should the assistant complete end to end, and which should it hand to a person?
• What information does it need to answer well — and where does that information live today?
• What must it never do, say or reveal?
• How will we know, in numbers, that it is helping?

The prompt is a specification. You cannot write a good one until you know what the system is for.

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2. Design the conversation, then the context

Conversational design is closer to interaction design than to copywriting. It means mapping dialogue flows and interaction paths, deciding how the system recovers when it misunderstands, and handling the multi-turn exchanges where meaning depends on what was said three messages ago.

Underneath the flow sits context modeling: deciding what the model should know at each turn. Too little, and it guesses. Too much, and it loses the thread or exposes information it shouldn’t. Prototyping against real scenarios — not idealized ones — is the fastest way to find the balance.

Design for the unhappy path

Most of a conversational system’s quality is decided in the moments it is unsure. Make uncertainty visible, ask a clarifying question, and offer a route to a person.