November 12, 2024
I remember the first day of my very first internship fondly. Making sure that I was prepped and professional looking for the new role, I donned a freshly ironed button-down shirt from Old Navy, reviewed the notes that I had taken during the interview process, and brushed up on some R-programming lessons. I looked ready to go. After taking the train downtown, passing the immense wall of the Lyric Opera Building that sits starkly against the edge of the Chicago River, I entered the lobby for the first time as a young professional.
I then proceeded to get lost in the elevator bank of the building and had to ask for help from at least 2 members of the building staff in order to be set on the right path.
In many ways the commercially available Large Language Models (LLMs) are more like the intern than a lot of us will admit. They are brimming with knowledge, technical skills, and ideas for content, but without a steady hand guiding them they are more likely to end up lost in the elevator bank than delivering business value to your company.
These LLM based assistants (ChatGPT, Copilot, Gemini, etc.) are designed to help organizations summarize information, answer questions, automate customer support, and enhance productivity in tasks like writing, coding, and data analysis. But, without the right context to use these tools, their effectiveness is low, leading to frustration.
One of the keys to companies successfully leveraging LLM’s is proper prompt / AI training. While these models are trained on terabytes of data, entire GitHub codebases, and libraries worth of books, organizations must also be trained to effectively wield their power.
I think of an LLM as your copilot — like that new intern needs direction, LLMs thrive with guidance.
Here are my 5 tips to get the most out of your LLM:
LLMs perform best when they understand your goals. Providing context and objectives in your prompt ensures that the LLM understands your direction and can give you results that are aligned with what you need. Instead of issuing vague prompts, clearly state the context and purpose behind your request. Think of it as giving directions — the more specific you are about your destination, the easier it is to arrive there.
Example: Instead of asking “Tell me about AI,” try “As if you were an experienced analyst summarize the latest trends in AI for a business audience interested in marketing automation.”
LLMs have the ability to generate a wide range of responses, so narrow your focus by specifying details such as tone, length, format, or audience. These specifics keep the LLM focused and produce output that matches your needs closely, making it a more reliable partner in your work.
Example: If you need a brief explanation, specify this in your prompt, like “Explain the transformer architecture used in Large Language Models in three sentences for someone without a technical background.”
If the initial response doesn’t meet your expectations, ask follow-up questions or refine your prompt with additional guidance. Flying with a copilot involves constant communication — adjusting course, clarifying instructions, and fine-tuning to stay on track. Similarly, follow-up questions with your LLM help it refine its output to match exactly what you’re looking for. This iterative feedback transforms the LLM from a one-time responder to a true collaborator, refining its answers in response to your guidance.
Example: After receiving an answer, you might say “Can you expand on this topic with examples relevant to the finance industry?” or “Can you simplify this explanation even further?”
Including examples in your prompts can significantly improve the relevance of the output. Showing the LLM the kind of response you’re looking for — whether it’s a specific format, tone, or level of detail — allows the model to understand your ideal outcome, making its responses more accurate and aligned with your standards. This makes it a much more effective copilot in helping you reach your goals.
Example: You could prompt with “Write a short blog intro about the benefits of AI for retail, similar to this format: [insert your example formatting].”
For more intricate requests, break down your prompt into step-by-step instructions. This approach guides the LLM through each phase, allowing it to produce responses that better address each part of your task.
Example: Instead of asking “Create a marketing plan for our AI product,” break it down into steps, like “List the key objectives, target audience, and promotional channels for a marketing plan focused on an AI product.”
Maximizing the value of an LLM isn’t just about what you ask, but how you ask. With the right guidance, your LLM becomes a valuable copilot, enhancing your work and helping you navigate complex tasks. AI may be evolving rapidly, but success with these tools still relies on a collaborative approach. By applying these tips, you’ll not only get better responses from your LLM but also increase its efficiency, saving time and resources. We recommend throwing an interactive “Prompt Party” within your organization to put these tips into practice. Actively engaging with LLMs can make them invaluable copilots rather than passive assistants.
For organizations looking to integrate LLMs and automation and enable their teams to use these technologies effectively, Quantum Rise can help. Our team is experienced in helping businesses leverage AI and automation to achieve their goals. Reach out to learn more about how we can support your journey in AI-driven innovation.
