AI Prompt Engineering

AI Can’t Pick Your Jury 

In the world of artificial intelligence, one of the biggest—and most misunderstood—myths is that language models can act like human search engines for people. Ask any generative model about an individual’s age, address, employment history, or voting affiliation, and you’re likely to get… not much. Despite their brilliance with text, story generation, and data interpretation, large language models (LLMs), such as Google Gemini, simply aren’t designed to perform people searches. That limitation becomes glaringly obvious when you try to apply them to specialized, real-world tasks like jury research.

Yet before we dive into why Gemini falls short in that domain, we need to understand a core skill — prompt engineering — and how it can make or break your AI results.

Prompt Engineering: The Art of Speaking AI’s Language

At its core, prompt engineering isn’t a mysterious hack — it’s about structuring your request to an AI in a way that maximizes clarity, context, and desired output. Think of the AI as a brilliant but forgetful assistant: give it clear instructions and relevant details, and it will perform better. Give it vague, generic prompts, and you often get disappointing or incorrect responses. 

In practice, effective prompt engineering should involve:

• Specific requests rather than broad questions

• Example formats that show the model what you want

• Role assignments — e.g., “You are a legal data analyst…”

• Context and constraints — providing background or limits

Why Prompting Matters

Without thoughtful prompting, even the most powerful AI can produce hallucinations — confident but fabricated outputs that sound real. These hallucinations aren’t quirks; they’re inherent to how LLMs are trained. Unlike traditional search engines, they don’t pull live web data unless explicitly configured to connect to a search API — and even then, the integration can be messy. 

That’s a key point we’ll revisit when examining why Gemini struggles with people-oriented searches.

What Gemini Is — and Isn’t

Google Gemini is a family of multimodal large language models developed by Google and DeepMind. It’s designed to generate text, interpret context, and answer a wide range of queries. The model has advanced language understanding and can even process images along with text, making it powerful for tasks like summarization, creative writing, or code generation. 

But here’s the critical distinction:

Gemini — like all current LLMs — is not a database of people information.

Unlike people search engines like LexisNexis or specialized investigative tools that compile public records like Vijilent, Gemini has no direct access to up-to-date, curated personal data about individuals. It doesn’t inherently know your age, where you live, your employment history, or other specifics unless that information is already public and codified in widely available sources. Even then, it can struggle to retrieve it. 

When you try to collect detailed information about real individuals, especially for purposes like jury research, this becomes a huge limitation.

To illustrate, I personally searched my own name in Gemini and asked it to find any public data information such as my age, previous cities I’ve lived in, and any education and work history. This is all information that I’ve previously found using the tactics at Vijilent to compile information from public resources.

The result of a Gemini search? Nothing verifiable. Gemini couldn’t find any public data— as this is not something the model is trained to retrieve correctly or reliably.

This isn’t a failure of AI ingenuity — it’s a fundamental limitation of how models like Gemini work. They’re trained on massive text corpora to predict language — not to act as dynamic institutional databases of people and personal data.

Jury Research and AI: A Tough Fit

When you try to use Gemini for jury research, here’s what typically happens:

• Results are missing: The model fails to pull factual data about individuals.

• Output is unreliable: If something does pop up, it may be made up or inferred based on patterns rather than facts.

• No structured sourcing: Gemini doesn’t automatically cite sources reliably unless asked very explicitly. 

Despite Google’s access to web search, the integration is so limited that the model struggles to gather accurate, timely information from actual pages, instead giving snippets that are incomplete or misleading. 

Gemini in Legal Tech: Pros and Cons

While people search isn’t Gemini’s strength, there are legal tech applications where it can shine:

Useful Legal Tech Capabilities

1. Document SummarizationGemini can quickly read and summarize contracts, briefs, depositions, and filings.

2.  Workflow AutomationAutomate repetitive tasks like discovery triage, email drafting, or document tagging.

3. Internal Data AnalysisWhen paired with a secure, internal dataset (e.g., firm documents), Gemini can act as a fast analytic assistant.

Google’s official prompt guide even includes examples of how legal teams can attach documents to Gemini Enterprise and ask focused tasks like locating case documents and summarizing testimony. 

Cons & Risks

1. Privacy & ComplianceConsumer versions can inadvertently expose private data or entangle user info with model training. Enterprise versions mitigate this but cost money. 

2. HallucinationsGemini can confidently produce false information, which is dangerous in legal settings if not verified. 

3. Lack of Structured People DataUnlike dedicated databases used in investigations or jury research, Gemini simply doesn’t index or store people records in a verifiable way.

The Bottom Line

Prompt engineering is essential if you want to get useful output from AI, especially for complex tasks. But it doesn’t change what these models are fundamentally built for: language prediction and pattern recognition — not people search.

For legal tasks like jury research, Gemini may be a helpful assistant when it’s used for summarization, case prep, or content generation — but it cannot replace dedicated tools designed to access structured public records or court databases.