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Day 10 – Limitations of Large Language Models Lesson
What you'll learn
Identify the most common sources of outdated answers and prompt the model to search the web with sources.
Prompt with stronger context so the model predicts more relevant responses instead of guessing wrong.
Verify outputs that may be hallucinated by asking for citations and cross-checking with traditional search.
Recognize signs of bias in training data and adjust your use to avoid passing that bias into your work.
Reduce privacy risk by changing training settings and avoiding sensitive inputs in your chats.
Build a simple habit of follow-up prompts that correct or refine the model when it drifts.
Lesson Overview
Large language models look smart, but they work by predicting the next likely word based on training data. That design creates five recurring limits you will face in daily use. First, outdated information. If the model does not go to the web, it can miss recent events and products. A simple fix is to ask it to search and to list a few sources you can check yourself.
Second, lack of true understanding. The model does not think like a person. It guesses. You can reduce bad guesses by giving rich context, clear goals, and consistent chats so it has a better base to predict from.
Third, hallucination. Sometimes the model fabricates details and presents them confidently. Treat research outputs as drafts. Ask for sources, click through, and compare with a regular search before you rely on anything.
Fourth, bias in training data. Models learn from data chosen by the companies that build them. If that data is biased, the model can inherit that bias. Awareness and careful review help prevent it from ending up in your work.
Fifth, privacy. Your inputs are sent to remote servers. Free plans may use your chats to train future models unless you turn that off. Business plans can disable training by default. Even with settings adjusted, avoid entering sensitive material.
Who This Is For
This lesson is for anyone using AI to produce information others will read or act on. If you use AI for research, writing, planning, or decision support, understanding these limits will save time and protect your work quality.
- Content creators drafting articles, scripts, or summaries
- Researchers and analysts checking facts and sources
- Educators preparing lessons or study materials
- Marketers producing briefs, outlines, and product copy
- Founders and small teams using AI for day-to-day tasks
- Operations and admin staff writing policies or FAQs
- Comprehensive, Business-Centric Curriculum
- Fast-Track Your AI Skills
- Build Custom AI Tools for Your Business
- AI-Driven Visual & Presentation Creation
Where This Fits in a Workflow
Use the techniques from this lesson any time you depend on an AI answer for something timely, factual, sensitive, or public-facing. Before you trust an output, run a quick check: did the model search the web, cite sources, and reflect the context you provided?
Two practical examples:
- Writing a tech review or market update. Ask the model to search and provide three sources, then compare those links against the draft it produced.
- Drafting internal policies. Provide detailed context about your company’s situation. Then scan the output for bias or fabricated claims, and strip out any sensitive details you never want in a chat.
These habits make AI a reliable helper rather than a risk.
Technical & Workflow Benefits
The old way is to accept AI outputs at face value, then fix mistakes after publication or delivery. That wastes time and can erode trust. The approach in this lesson builds simple checkpoints into your process so you work faster without sacrificing accuracy.
- Outdated info becomes manageable when you prompt the model to search the web and list sources you can verify in minutes.
- Lack of understanding is reduced by giving rich context, which lowers off-target guesses and shortens revision cycles.
- Hallucinations are caught early by asking for citations and cross-referencing with a quick search, which prevents rework later.
- Bias is less likely to slip in when you know to scan for loaded claims and ask the model to present multiple perspectives.
- Privacy risk drops when you turn off training where possible and avoid sharing sensitive details in prompts.
These simple changes improve clarity, reduce rework, and raise the quality of outputs you can stand behind.
Practice Exercise
Use a recent topic the model may not know by default, such as a product released in the last few weeks or a current event in your field.
- Step 1: Ask the model for a short summary of the topic. Then follow up with a prompt that says to search the web and provide three sources. Compare the first draft against what the sources say and note any differences.
- Step 2: Ask the model to rewrite the summary with the correct details from the sources. Request that it include the links at the end.
- Step 3: Test for hallucination and bias. Ask a loaded or niche question related to the topic, then require citations. Check whether the sources actually support the claims.
Reflection: Where did the model get things wrong or guess? Which follow-up prompt gave you the biggest improvement: asking for sources, adding context, or correcting bias?
Course Context Recap
This lesson sits at the point in the boot camp where you have used several AI tools and are now strengthening reliability and safety. Earlier lessons showed you how to get value from large language models. Here you learned how to spot limits and apply quick fixes. Next, you will build on these habits as you create stronger prompts and workflows that hold up under real deadlines. Continue through the course to practice these checks in more advanced tasks and keep improving your results.