Why Your LLM Interactions Fail – Treating AI as a Chatbot Instead of a Data Product
The Conversational Trap
In the current rush to integrate Generative AI into the enterprise, most corporate leaders have fallen into what I call the “conversational trap.” Because interfaces like ChatGPT, Claude, and Gemini look and feel like instant messaging apps, we naturally treat them as such. We engage in back-and-forth volleys, asking a simple question, getting a mediocre answer, and then trying to “fix” it through a series of follow-up messages.
I view this approach as a fundamental misunderstanding of the technology. To achieve enterprise-grade results that actually move the needle on productivity, you must stop treating Large Language Models (LLMs) as digital assistants and start treating them as Data Products.
The Illusion of the "Smart" Chatbot
A common frustration among users is that AI seems brilliant at first, but the “wheels come off” the longer a session lasts. You might start with a clear objective, but by message ten, the model is hallucinating, forgetting previous instructions, or providing generic, unhelpful advice.
This isn’t a glitch; it’s a byproduct of how these models are engineered. Many modern LLMs are heavily optimized for one-shot prompts. Developers optimize them to appear incredibly “smart” on the very first interaction to impress the general public and gain market share.
However, as a chat history grows, the “context window”—the amount of information the model can keep in its active “memory”—becomes cluttered with fragmented instructions, previous errors, and redundant clarifications. This leads to context drift. The more you attempt to “chat” your way to a solution after a poor start, the more likely the AI is to lose the thread of your original business objective.
Quality In, Quality Out: The Data Product Mindset
In traditional data engineering, the rule is absolute: Garbage In, Garbage Out (GIGO). If your input data is unstructured, dirty, or lacks context, your analytics will be flawed.
An LLM is, at its core, a sophisticated transformation engine. It takes an input (your prompt) and transforms it into an output (the completion). If you treat that input as a “data packet” rather than a casual greeting, the quality of the output shifts dramatically. By front-loading a high-quality, structured “schema” in your first message, you drastically increase the probability of a successful first-time result.
To move from “vibes-based” prompting to engineering, I utilize specific frameworks that ensure every necessary variable is present before the model begins its work.
Let Your Data Work For You
Implementing Structural Rigor: SEED and CRIT
I recommend two primary prompting frameworks for interaction with these LLMs.
1. The SEED Framework
The SEED framework is ideal for creative transformations, project kick-offs, or when you need the AI to synthesize multiple ideas into a cohesive whole.
- S – Situation: Clearly define the business context. What is the current challenge? Who are the stakeholders?
- E – End Goal: Describe the final transformation. What does success look like once this task is complete?
- E – Examples: Provide illustrative, clarifying ideas. Show the AI the style, tone, or logic you expect.
- D – Deliverables: Specify the exact format. Do you need a 5-bullet summary, a JSON object, or a 1,000-word report?
2. The CRIT Framework (My Favourite)
For complex business tasks and technical problem-solving, the CRIT framework is the gold standard. It forces the AI into a professional role and prevents it from making assumptions.
- Context: Fully state the environment. Include relevant links, what has already transpired in the project, and a clear statement outlining the objective of this specific interaction.
- Role: Assign a real-world role grounded in reality and training data (e.g., “You are a Specialist Marketing Consultant focusing on SaaS growth metrics”).
- Interview: This is the game-changer. Explicitly instruct the AI to ask you up to 3 clarifying questions to improve its context before it starts the task. This ensures the “data product” has the required inputs.
- Task: Give a detailed, unambiguous action request that will make you happy as a user if the AI delivers this.
Leading the AI-Driven Organization
For the modern executive, the takeaway is clear: Success with LLMs is a design challenge, not a conversation. By implementing frameworks like CRIT or SEED, you are essentially creating a “Data Contract” between your team and the AI. You reduce the noise of long, drifting chat histories and ensure that your organization is treating AI with the same rigor it would apply to any other mission-critical data pipeline.
The next time you open an LLM to solve a business problem, don’t just start a conversation. Define the role, provide the context, and demand an interview. When you give the model quality to work with on the first message, you aren’t just “prompting”—you are engineering an outcome.
