Blog: 10.09.24

Unlocking the Potential of AI and Large Language Models (LLMs) in Investigative Work (Part 1)

Large Language Models (LLMs) like ChatGPT have become household names, celebrated for their ability to generate text, answer questions, and perform various tasks with ease. While many have explored their uses for productivity and entertainment, the potential for LLMs in investigations and intelligence work is particularly noteworthy. However, to harness their full power, it's crucial to understand what LLMs can and can't do, and how to use them effectively.

What Are LLMs and How Do They Work?

LLMs, or Large Language Models, are a type of AI under the umbrella of Generative AI. These models are trained on massive datasets, analyzing billions of data points to generate text-based outputs. When you input a prompt, LLMs use complex algorithms to predict and produce relevant text, images, or even audio. In essence, they are powerful tools that can interpret and generate content based on the data they’ve been trained on.

Strengths of LLMs

LLMs excel in tasks such as summarizing and synthesizing content, formatting and styling text, analyzing and visualizing data, and translating languages. For investigators, this means LLMs can be invaluable for condensing large volumes of reports, analyzing datasets, and even identifying patterns in data. These capabilities can significantly enhance the efficiency and effectiveness of investigative work.

Limitations of LLMs

Despite their strengths, LLMs are not without limitations. They lack the ability to perform logical reasoning and can be easily misled by false inputs. Their outputs, while convincing, are based purely on probabilistic predictions rather than true understanding. Additionally, LLMs can produce "hallucinations"—instances where the model generates incorrect or fabricated information. This makes them unreliable for tasks requiring factual accuracy or logical analysis.

Best Practices for Using LLMs in Investigations

To make the most of LLMs in investigative work, consider these best practices:

Provide Clear Context: Give the LLM detailed instructions and context for your request. For example, you can ask it to assume the role of a crime analyst, focusing on specific aspects of a case. The more precise your instructions, the more relevant the output will be.
Structure Your Prompts: Clearly outline what you want the LLM to do with the input data and how you want the results formatted. This approach, known as prompt engineering, can significantly improve the quality of the LLM's output.
Example: Instead of a vague request, try: “Imagine you are an intelligence analyst at a financial crime agency. Analyze the following data for indications of money laundering, and provide a structured report with BLUF (Bottom Line Up Front), suspicious indicators, and recommendations.”
Leverage Advanced Features: Use LLMs to automate repetitive tasks, such as generating Boolean strings for searches, or drafting complex queries in the correct syntax. This can free up time for more critical analytical work.

The Future of LLMs in Investigations

We are only scratching the surface of what LLMs can achieve in investigations and intelligence. By understanding their strengths and limitations and applying best practices, investigators can leverage these tools to enhance their work, automate routine tasks, and uncover insights that might otherwise be missed.