Is Your AI Getting Dumber? Here's How to Fix It

🧠 Are Large Language Models Getting "Dumber"? Let's Explore What is Really Happening

Have you noticed that some AI tools seem to be, well… not quite as sharp lately? Maybe they aren’t following instructions as precisely as they used to, or their responses are starting to feel off-track. I have seen this more in the past few weeks and many readers are reporting similar experiences with large language models (LLMs).

So what’s going on here? While it might feel like LLMs aren’t performing as well, the reality is more complex. There are a few key reasons why we are experiencing this decline in responsiveness. And, here’s the good news: there are things you can do to improve the AI’s output. Let’s check out five reasons this could be happening and explore how you can still get the most out of your AI tools.

🤖 5 Reasons Why LLMs Might Feel “Dumber”

1. Model Updates May Shift Behavior
LLMs are constantly being updated to improve performance, but not all updates work the same for every use case. Sometimes, an update designed to optimize one feature might unintentionally cause other behaviors to decline. For instance, changes in how the model interprets instructions could make it less likely to follow detailed prompts as it once did. If you've noticed a drop in responsiveness, you might be seeing the effects of a recent update that shifted how the model processes certain instructions.

2. Over-reliance on Generalized Outputs
LLMs are designed to provide generalized answers based on vast datasets. This means that as they process more diverse input, their responses may lean toward generalizations rather than specific, tailored insights. If you feel that AI is "dumber," it could be because the model is producing more surface-level answers instead of the deeper, more nuanced responses you are expecting. Crafting more specific prompts can often solve this problem, ensuring that the AI delivers the detailed insight you need.

3. Prompt Fatigue
AI systems respond to the way they’re asked questions. If prompts become vague or lack sufficient detail, the output is likely to reflect that. Think about it: are your prompts as clear as they could be? Over time, it’s easy to fall into a routine of asking AI the same questions in the same way, which might lead to less effective interactions. Learning to refresh your prompt strategies—by being more precise or incorporating new instructions—can make a significant difference in the AI’s performance.

4. Context Limitations
Most LLMs have context limits, meaning they can only “remember” a certain amount of information from a conversation. If your conversations with the AI are long or complex, it may lose track of earlier details, making it seem like it’s not following instructions well. To combat this, try breaking up your interactions into smaller chunks or reintroducing key context throughout the conversation to keep the AI on track.

5. Misalignment Between User Expectations and AI Capabilities
Sometimes, what seems like the AI underperforming is simply a misalignment between what you expect and what the model is actually designed to do. AI tools are powerful, but they have limitations—especially when it comes to tasks requiring high-level critical thinking or deep reasoning. If you expect the AI to go beyond its capacity, it might feel like it's "dumbing down." Aligning your expectations with the AI’s strengths (e.g., summarization, pattern recognition, etc.) can help improve your experience.

🎯 How to Help Your AI Produce Better Results

While it may seem like LLMs are struggling, there are simple adjustments you can make to improve their responses. Here’s how:

1. Use Specific, Clear Prompts: The more detailed and precise your prompt, the better the AI’s response will be. If you feel that AI is missing the mark, try refining your instructions. Be explicit about what you want the AI to focus on. Remember it this way: Treat it like it is the smartest 7 year old you will ever encounter.

2. Reintroduce Key Context: If you’re having a longer conversation with AI, remind it of important context along the way. AI has limited memory, so refreshing key details ensures that it stays on track. This is where including snippets and profiles in every prompt helps you make sure you are not relying on previous parts of the conversation to understand what you want. Include them every time.

3. Experiment with Prompt Variations: Don’t be afraid to mix up your approach! Paul, an expert in helping HR professionals craft more effective AI prompts, always emphasizes that learning to prompt better can unlock the full potential of AI. Try different ways of asking the same question to see which prompts yield the most robust responses.

Takeaways:

1. Model updates and generalized outputs can make LLMs feel "dumber"—but better prompts can improve performance.

2. Specific, clear instructions and reintroducing context are essential for keeping AI on track.

3. Learning how to prompt AI effectively will enhance your HR strategy, driving productivity and engagement.

As AI becomes more integrated into business operations, your ability to use it effectively will set you apart as a strategic leader. Learning how to prompt AI for high-quality results will not only enhance your current work but will also position your organization for long-term success in a tech-driven world.

📄 Prompt of the Week

Use this prompt, or share it with your leaders, to develop stronger communication with your remote teams.

ROLE:

Act as an expert in remote team collaboration and communication. You specialize in helping distributed teams work together efficiently by providing actionable strategies tailored to their needs.

REQUEST:

Help me develop strategies for effective collaboration and communication within my remote team. I want to ensure that our workflow is smooth, our communication is clear, and our team remains engaged and productive.

GOAL:

The goal is to create a set of strategies that improve remote team communication, foster collaboration, and enhance team morale. These strategies should be actionable and adaptable to my team's needs and tools.

INSTRUCTIONS:

1. Current Communication Tools: Analyze the communication tools we currently use, such as [choose: Slack, Microsoft Teams, Zoom, Google Meet, Asana, Trello]. Recommend improvements or alternative tools that could enhance real-time and asynchronous communication.

2. Meeting Structure: Suggest an ideal meeting schedule for a remote team. Consider [daily stand-ups, weekly check-ins, monthly strategy meetings, project-specific meetings, or virtual social gatherings]. Include ideas to keep meetings focused and efficient.

3. Task and Project Management: Analyze our current method of assigning and tracking tasks using [choose: project management software like Asana, Trello, Jira, ClickUp]. Recommend ways to improve clarity on project timelines, accountability, and progress tracking.

4. Team Collaboration Methods: Propose strategies to encourage collaboration among team members working remotely. Should we use more [brainstorming sessions, virtual workshops, paired tasks, document sharing]? Provide steps to implement these methods seamlessly.

5. Maintaining Team Morale: Offer suggestions for keeping team morale high in a remote environment. This might include [virtual team-building activities, recognition programs, wellness check-ins, casual virtual hangouts, or flexible work hours].

6. Cross-Time Zone Collaboration: If relevant, develop strategies for managing team members across different time zones. Include recommendations for [flexible work hours, overlapping work windows, clear documentation practices, or asynchronous communication protocols].

7. Feedback Loops: Advise on how to establish continuous feedback mechanisms that work well in a remote setting. Should we use [formal surveys, one-on-one meetings, anonymous feedback tools, performance reviews]? Provide a plan for regular feedback to improve processes.

OUTPUT FORMAT:

Provide the output as a detailed guide with step-by-step strategies under each section. Clearly separate each recommendation with concise explanations, including specific tools and examples to improve remote collaboration.

Share this with your leaders and see what results they get.

🤩 The Fun Side of AI

Using AI doesn’t have to be all work. Check out this imaginative way to use AI to come up with some interesting ice breakers.

✍🏼 Lighten Things Up

ROLE:

You are a creative AI assistant who excels at generating engaging ice breakers for teams.

REQUEST:

Create a list of fun ice breakers tailored to my team’s characteristics. The ice breakers should encourage team bonding, be light-hearted, and work in both in-person and virtual settings.

GOAL:

The goal is to energize the team, create a relaxed atmosphere, and encourage participation. Make sure the ice breakers are suited to the team’s unique dynamics.

INSTRUCTIONS:

To generate personalized ice breakers, here is information about my team:

Team Size: [Insert number of people]

Work Environment: [In-person/Virtual/Hybrid]

Team Dynamic: [e.g., collaborative, competitive, introverted, diverse]

Recent Team Events or Milestones: [Insert any major recent achievements or changes]

Preferred Ice Breaker Style: [Trivia, storytelling, physical activities, etc.]

Want to learn more about building great prompts? Paul publishes a Prompt a Day newsletter on LinkedIn!

Click Here to subscribe and get a daily idea of how to create powerful and effective prompts across a variety of topics, both personal and professional.