Top 5 Rookie Mistakes to Avoid as a Legislative Staffer Using a GenAI LLM
Generative AI Large Language Model (GenAI LLM) tools like OpenAI’s ChatGPT, Anthropic’s Claude, and Google’s Gemini have the potential to become invaluable resources for Congressional and Parliamentary staff juggling constituent services, policy research, communications, and legislative brainstorming. But like any tool, their effectiveness is greatly dependent upon the user’s knowledge and skill of how to use them. Too often, individuals treat these chatbots like magic “black box” question-and-aswer machines rather than what they actually are: sophisticated assistants that require clear direction, context, and oversight to perform well. Here are five common mistakes we see — and how you can avoid them.
1. Quoting an LLM as a Source
Rookie Mistake: Citing “ChatGPT” or “Claude” in a memo, talking points, or research document as if it is an authoritative source.
What This Looks Like: “According to ChatGPT, the Infrastructure Investment and Jobs Act allocated $65 billion for broadband expansion.” Or including “Source: ChatGPT” at the bottom of a policy brief.
Correct Approach: Think of a GenAI LLM like Google — it is a tool that helps you find information, not the source itself. Any facts, figures, or claims an LLM provides should be traced back to their original source, double checked, and cited appropriately.
What This Looks Like: Use ChatGPT, Claude, or your institution-approved LLM of choice to help you locate information quickly, then verify it. Ask:”Please help me find the broadband funding amount in the Infrastructure Investment and Jobs Act passed in 2021 and point me to where I can verify this?” Then track down the actual bill text, CRS report, or agency document, double check the information the LLM provided, and cite where the information originated. Your citation becomes: “Infrastructure Investment and Jobs Act, Pub. L. 117-58, Division F.”
2. Not Providing Context for Your Question
Rookie Mistake: Asking questions without explaining — in general terms – who you are, what you're working on, or why you need the information.
What This Looks Like: Simply typing “draft testimony on education funding” with no additional details.
Correct Approach: Use your LLM tool to the best of its ability by providing relevant context about your role, your boss' priorities, your audience, and your constraints.
What This Looks Like: “I'm a legislative assistant for a Republican Member in the US House of Representatives from a rural district in the Midwest. I need to draft a three-minute oral opening statement for a subcommittee hearing on Title I education funding. My boss is concerned about ensuring small, rural school districts aren't disadvantaged by population-based formulas. The audience is the Education and Workforce Committee, on which my Member is a majority member. Please help me draft an opening that emphasizes equity for rural schools and gratitude for having a hearing on this important subject.”
3. Treating the First Response as the Final Answer
Rookie Mistake: Treating an LLM like a search engine by using the tool’s initial response without refinement or follow-up.
What This Looks Like: Asking “What are the main arguments against this bill?,” getting a generic four-paragraph response, copying it into your document, and calling it done.
Correct Approach: View the LLM’s first response as a draft or starting point. Push back, ask clarifying questions, request different angles, or tell the AI tool what's not working. These tools improve dramatically with iteration and being able to converse with the tool as if it were an assistant or brainstorming partner is one of the greatest advantages of this technology.
What This Looks Like: After getting that initial response, you follow up: “The third argument feels too vague—can you make it more specific to healthcare access in rural areas? Also, I need a counterargument to each of these points because my boss may face these questions during policy debates. Lastly, please make the tone more conversational? Imagine the use case for this information being a prep document for a town hall, not a policy memo.”
4. Using It for Tasks Requiring Real-Time or Highly Specific Legislative Data
Rookie Mistake: Assuming any LLM has access to current bill text, today's vote counts, recent committee activity, up to date information on news or current events, or knows the specific details of legislation from this Congress.
What This Looks Like: “What's in H.R. 2847?” or “Did the Senate pass the appropriations bill yesterday?” or “What amendments were offered in committee this morning?”
Correct Approach: It is vital to understand that AI models have knowledge cutoffs (often months old) and do not access live databases. Knowing these limitations empowers you to make better choices when trying to find specific types of information. For example, this could be turning to Congress.gov for up to date legislative information or to your committee resources or CRS for legislative analysis. Remember, though, that you can then turn back to using the LLM to summarize the resources you find (assuming that they are public, not classified, or non-sensitive in nature).
What This Looks Like: First, pull the current bill text from Congress.gov. Then you prompt: “I'm attaching the text of H.R. 5731 from the 119th Congress, the School Food Modernization Act. Please summarize the key provisions in three bullet points suitable for a press release, highlighting what's new compared to previous school nutrition that has been introduced since 2011 (the 112th Congress)?’
5. Forgetting That A Tool (No Matter How Advanced) Can't Replace Subject Matter Expertise or Judgment
Rookie Mistake: Treating AI-generated analysis as a substitute or shortcut for determining your own political judgment, institutional knowledge, or understanding of your Member's priorities.
What This Looks Like: Asking “Should my boss support this amendment?” or “What's the best political strategy here?” and implementing whatever the AI suggests without applying your own expertise.
Correct Approach: Use GenAI LLMs as a tool to augment your work — to brainstorm options, draft language, or organize research — but apply your own judgment about political feasibility, your boss' voting record and values, coalition dynamics, and district priorities. These tools can be helpful in boosting your capacity of accessing and processing information, but at the end of the day, you are responsible for thinking through the information that is presented and making the choices that best serve you and your boss. You are responsible for your work and all final products, regardless of what tools you use along the way.
What This Looks Like: Instead of asking an LLM to make strategic decisions, use it as a thought partner: “I'm considering three different approaches for my boss on this amendment: supporting it outright, offering modified language, or opposing it. Can you help me outline the pros and cons of each approach? I'll need to consider that my boss sits on the committee, represents a swing district, and has historically been cautious on this issue.”
The Bottom Line
GenAI tools have the potential to act as powerful assistants for Congressional and Parliamentary staff, but it is vital to remember: they are just one type of tool at your disposal. It takes practice and intention to learn their strengths as well as understand their limitations. These five common rookie mistakes highlight key fundamentals for successful GenAI LLM use: they need direction, verification, and human judgment. For more tips on how to use these tools successfully in a legislative professional setting, check out our ever-growing catalog of resources at popvox.org/ai.
