Understanding Large Language Models and Prompt Engineering

Let’s start with the basics: Prompts and Large Language Models (LLMs).

LLMs are sophisticated machine learning models that leverage exceptionally large datasets to understand and generate human-like text. They have evolved from simple models interpreting word-based interactions to intelligent machines simulating complex, context-rich conversations and analytical frameworks.

But how do we instruct these LLMs to process information and deliver the content we need? Here's where prompts come into play. Prompting is the process of creating an input that will help determine a LLMs output. Be we don’t just prompt, we engineer. Instead of asking a model a straightforward question, we 'engineer' or carefully craft the prompt to nudge it towards producing a better output.

Not used an LLM before? Have a chat with the one, created by one of our founders, below 👇

https://gpt-chatbot.glitch.me

Utilizing the Analysis Frameworks

With an understanding of the fundamentals of LLMs and prompt engineering, we can transition into practical applications.

We’ve provided a robust range of analysis frameworks, organized by category and format, and designed to assist professionals across varied domains. To employ these frameworks effectively, simply

1. Copy the specified prompt text and input it into your selected Large Language Model.
2. Modify the prompt if needed, to better align with your specific situation and objectives.
3. Incorporate the specific context and details of your ongoing project or strategic challenges.

When using these prompts, ensure you include as much detailed information as possible, such as the scenario at hand, targeted goals, constraints, and scope. This ensures the output is as relevant and actionable as possible.

5 tips for prompting:

1. Be Specific: Being more explicit and detailed in your prompts will guide the model to produce more accurate results. Treat a LLM like a teenage who needs explicit instructions.
2. Prime your LLM: Instruct your model to adopt an expert's voice in your specified field such as "As an expert in business analysis, how would you...?". This reinforces the context and encourages more appropriate answers.
3. Give Context: Providing the model with background information or specific instructions can significantly improve the relevance of the output. The more context you provide within your prompt, the more likely the model will give you an appropriate and useful ouput.
4. Experiment with Formats: The same query can be posed in various ways - as a question, a statement or a request, each soliciting a slightly different response. Use this to your advantage.