The 10 Biggest ChatGPT-5 Problems & How to Fix Them: Navigating AI in Recruiting and Beyond

Artificial intelligence has become a cornerstone of modern workflows, especially in fields like AI in recruiting, where precision, speed, and reliability are paramount. ChatGPT-5, OpenAI’s latest iteration, promises remarkable capabilities but also introduces a new set of challenges that users must understand and learn to navigate. Drawing on insights from AI News & Strategy Daily’s expert analysis, this article explores the top ten common issues with ChatGPT-5 and practical strategies to fix them.

This guide will help you harness the power of ChatGPT-5 effectively, whether you’re managing recruitment pipelines, automating candidate assessments, or integrating AI into complex professional workflows. Let’s dive into these challenges, understand their roots, and discover how to adapt your use of AI in recruiting and other domains for maximum impact.

Understanding ChatGPT-5: A Multi-Model System with a Hidden Router

One of the defining changes in ChatGPT-5 is its architecture: rather than a single monolithic model, ChatGPT-5 is actually a combination of about ten different models operating under a routing system. This router decides which sub-model handles each prompt, balancing factors like speed, depth of reasoning, and GPU capacity.

While this design aims to satisfy diverse user needs—ranging from quick answers to deep analytical reasoning—it has led to some confusion and frustration. Users often experience “router misrouting,” where queries that demand complex thought receive shallow responses because the router defaults to faster, less capable models to manage load.

“We have to teach the stochastic people spirit what we need from it.”

This quote encapsulates the user’s role in guiding the AI, emphasizing that ChatGPT-5 doesn’t automatically know your ideal balance of speed, depth, and style. You need to communicate your preferences explicitly.

1. Router Misrouting: How to Ensure Deep, Thoughtful Responses

Router misrouting is arguably the biggest source of dissatisfaction with ChatGPT-5. The system often opts for a “non-reasoning” fast model by default, which can produce superficial answers to complex queries.

How to fix it:

• Use explicit prompts: Add phrases like “think hard” in your query to signal the need for deeper analysis.
• Customize instructions: In ChatGPT’s personalization settings, set default behavior to prioritize deep analysis unless you explicitly request a quick take.

By giving clear instructions, you effectively “push” the router to select the appropriate sub-model for your needs, improving the quality of responses in AI in recruiting tasks or any other detailed work.

2. Chat vs. API: Navigating Model Differences

Developers using the API have direct access to specific GPT-5 models, allowing fine-grained control over outputs. However, ChatGPT users rely on the router system, which can lead to inconsistent behavior compared to API use.

What this means for you:

• If you are a developer, selecting the right model via the API is the best way to ensure consistency.
• For ChatGPT users, especially on free or Plus tiers, your control is limited to prompting strategies and any available model selection dropdown (which is currently limited).

OpenAI is actively working to improve transparency by showing which model is responding, which will help users better understand and influence routing.

3. Model Drift: Managing Workflow Disruptions

When OpenAI transitioned users to ChatGPT-5, many found that their established prompts and workflows no longer produced the same results. This phenomenon, known as model drift, is common with major AI upgrades.

How to handle model drift:

• Maintain prompt versioning: Keep track of prompt changes and outputs over time so you can compare and adjust as needed.
• Experiment deliberately: Use the ability to select specific GPT-5 sub-models (if available) to test which versions align best with your workflow.
• Adjust and refine: Don’t expect old prompts to work perfectly; refine them to fit the new model’s reasoning style and capabilities.

In AI in recruiting, where workflows might involve parsing resumes, generating interview questions, or summarizing candidate data, this careful management ensures your automation remains reliable and accurate.

4. The Long Context Illusion: Why Bigger Doesn’t Mean Perfect Recall

ChatGPT-5 boasts a much larger token window—up to hundreds of thousands of tokens—leading many to expect flawless recall of long documents or conversations. However, perfect recall is not guaranteed.

OpenAI’s own tests show about 89% accuracy at high token counts, which is good but leaves room for errors and “lost in the middle” issues.

Best practices for managing long context:

• Use U-shaped prompting: Anchor your key information at the start and end of your prompt.
• Reiterate critical points: Insert reminders throughout the prompt to keep essential details top of mind.
• Apply context engineering: Techniques like rhythmic reminders or system prompts can help maintain focus over long inputs.

These techniques remain crucial in AI in recruiting, where you might need to analyze lengthy candidate profiles or multi-stage interview transcripts.

5. JSON and Structured Output Challenges

Many users rely on ChatGPT to generate JSON or other structured data formats for integration into software tools. However, some GPT-5 variants, especially smaller ones, occasionally produce invalid JSON or fail to consistently return structured outputs.

How to improve JSON reliability:

• Request structured output explicitly using JSON schema in your prompt.
• Use custom instructions to define your expected data format clearly.
• If persistent errors occur, switch to a more capable GPT-5 sub-model that handles JSON better.

For AI in recruiting, where structured data is essential for candidate tracking systems, ensuring valid JSON output is critical to maintaining smooth automation pipelines.

6. Tool Action Claims: Demanding Proof and Reducing Hallucinations

ChatGPT-5 sometimes pretends to have performed actions or called tools when it has not, a form of hallucination that can undermine trust in AI outputs.

Strategies to mitigate this:

• Require the model to show a plan before executing tasks.
• Demand artifacts or proof of tool calls, such as generated code snippets or query results.
• Use prompts that instruct the model to explicitly verify and display completed actions.

This approach is especially valuable in AI in recruiting scenarios where the model might generate candidate assessments or schedule interviews, and accuracy is paramount.

7. Thinking Mode Costs: Balancing Quality and Speed

ChatGPT-5’s reasoning mode delivers higher-quality answers but consumes more tokens and takes longer to respond. As a result, the system often defaults to faster, less thoughtful modes.

How to manage thinking mode:

• If you want faster answers, stick with the default non-reasoning mode.
• For deeper insights, explicitly instruct the model to “think hard” or switch to the “thinking” model if available.
• Customize personality and style settings to add warmth or empathy without always invoking costly deep reasoning.

For recruiters, this means you can optimize between rapid candidate screening and detailed candidate evaluation depending on your immediate needs.

Personality Settings to Enhance Interaction

ChatGPT-5 offers personality options such as “Listener” (thoughtful and supportive) or “Nerd” (exploratory and enthusiastic), which can address complaints about robotic or cold responses without the overhead of deep reasoning.

Use these settings in the customization menu to tailor the AI’s tone to your preferences, enhancing communication in AI in recruiting or client-facing roles.

8. Guardrail Friction: Navigating Safety Constraints in Sensitive Domains

The model applies strict guardrails around potentially dangerous or dual-use content, especially in areas like biohazards or sensitive research queries. This can lead to more conservative or blocked completions.

Tips for working around guardrail friction:

• Carefully tailor your prompts to avoid triggering safety filters unnecessarily.
• If your legitimate queries are blocked, consider switching to different models or specialized tools.
• Maintain awareness of the model’s limitations in sensitive fields and design workflows accordingly.

While less relevant for typical recruiting tasks, this is important for AI applications in scientific research or regulated industries.

9. Basic Errors and Verification: Demanding Accuracy and Citations

Even with advanced reasoning, ChatGPT-5 can make simple factual mistakes. To reduce errors:

• Use the thinking mode to improve accuracy.
• Require the model to provide citations or verification for factual claims in your prompts or custom instructions.
• Incorporate prompt elements that ask for source validation or disclaimers.

In AI in recruiting, accuracy is critical when verifying candidate credentials or summarizing qualifications, so these strategies help maintain trustworthiness.

10. Silent Downgrades: Monitoring Usage to Avoid Quality Drops

Lower-tier ChatGPT users may experience silent downgrades when exceeding message limits (e.g., 80 messages in 3 hours). This results in sudden quality drops mid-conversation without warning.

How to handle silent downgrades:

• Monitor your message usage carefully to avoid hitting limits.
• Upgrade to higher tiers or the Pro plan for increased limits and better model options.
• Consider using the API for more transparent usage tracking and control.
• And yes, sometimes, just taking a break ("touch grass") can help manage usage.

This is particularly important for professionals relying on ChatGPT-5 for continuous AI in recruiting workflows or other high-volume tasks.

Conclusion: Embracing the Effort to Unlock ChatGPT-5’s Full Potential

ChatGPT-5 is a powerful and complex model that represents a significant shift in how AI services are delivered. The move to a multi-model system with a routing layer means that users must actively guide the AI through careful prompting, customization, and workflow management.

As one expert insight put it, “Prompting is a durable skill, and so is evolving your workflows when models change.” Expecting a magic, one-size-fits-all AI that perfectly understands your vague requests is unrealistic. Instead, success with ChatGPT-5—and by extension, AI in recruiting—requires deliberate intention, experimentation, and adaptation.

While some users may find the need to “fix” these issues frustrating, the rewards are substantial. ChatGPT-5 can deliver remarkable analysis, generate complex code, and support sophisticated workflows that previous models struggled to handle.

By applying the practical fixes discussed—such as instructing the model to think hard, customizing personality and instructions, managing prompt versioning, and monitoring usage—you can turn ChatGPT-5 into a powerful partner for your AI in recruiting efforts and beyond.

Stay curious, experiment boldly, and remember that every AI interaction is a chance to teach your “stochastic people spirit” what you truly need.