Context Is King!
Have you heard that before?
Context is the prompt provided to a large language model (LLM). That prompt is then used by the LLM to generate its response. Few-Shot, Chain-of-Thought, ReAct, RAG — all of these are manifestations of Context Is King. The quality of content generated by an LLM is directly dependent on the quality of the context it’s given. If the LLM is a vehicle, context is the mechanism to use to operate that vehicle.
This is a vital concept to understand when working with LLMs, and one that’s vastly underestimated. Let’s do some light exploration of context by simply making sure we state our preferences.
The Situation: Writing an Email
Prompt:
Help me write a follow-up email to this: <previous email>
Context Analysis:
In this case, I’ve provided the previous email, which sets up who I’m talking to and what was said last time. There’s a task request, sure, but I haven’t stated any preferences so the result will be pretty generic. I’ll probably need to do a lot of modifying and I might not use much of the LLM’s original response.
New Prompt:
Help me write a follow-up email. This is for a client who hasn’t responded in a week and I want to emphasize, politely, that we’re waiting for their response to move forward. Make it clear that everything else is in place. <previous email>
Context Analysis:
Much better! Now I’ve added a preference for the email’s tone and the reason I’m sending it. This is going to provide a much better start to our response and give the LLM a suggested approach to professionally nudging the client.
The Situation: Meeting Summarization
Prompt:
Summarize this meeting: <transcript>
Context Analysis:
Ahhh, the oft underfed “summarize” task. While the generic summary an LLM provides isn’t usually bad, it can be so much better. So let’s add some preferences.
Prompt:
Summarize this 45-minute meeting for our internal team newsletter focusing on decisions made, project deadlines, and next steps. Note any tangents from the agenda and provide the time spent on-topic versus time spent off-topic. Here is the transcript: <transcript> and here is the agenda: <agenda>
Context Analysis:
Way better. Here, I’m being clear about what I specifically want from the meeting summary, and I’ll get back a focused, meaningful response. I’m also getting more evidence that Bob just won’t stay on topic. Come on, Bob!
As shown above, it doesn’t take much. I just ask myself, “Do I have any preferences for this?” And really… I do. So I express myself! I get particular. And I appreciate the generous returns for such minimal labor.
Why Context is the Current That Powers Enterprise AI
At 7Rivers, I treat context like a strategic asset. It’s how I unlock performance from language models and turn raw data into rich outcomes. Our Data Native™ model ensures every prompt, every application, every accelerator is designed with purpose and clarity.
Whether it’s GenAI-driven client interactions, internal productivity tools, or LLM-infused applications, I help businesses bake context into every layer of their AI systems. The result? Faster decisions, smarter automation, and better business value from your data.
Ready to move upstream with meaningful context? Let’s make your AI work smarter, not harder. Connect with 7Rivers and channel your data into real business outcomes
