Note:

I am by no means an AI expert, but recently, I’ve had the opportunity to experiment with some of its applications directly on our platform at Nuvitia.

What I’m sharing here is not the result of academic or expert analysis but rather observations from hands-on trials and experiments.

My goal is to spark discussion and gather feedback from those with more experience or different perspectives. Any insights or suggestions are more than welcome!

Introduction

AI language models like DeepSeek have revolutionized how we access technical knowledge, offering rapid solutions for tasks ranging from coding to system administration. However, these models are not infallible. They occasionally produce hallucinations—plausible-sounding but factually incorrect responses. In this article, we dissect a real-world example involving the backup tool Restic to explore why hallucinations occur, their risks in technical workflows, and how users and developers can address them.

The Incident: A Hallucinated Restic Parameter

In a recent interaction, I asked DeepSeek how to compare changes between two Restic backups while ignoring metadata.

The initial response incorrectly suggested using a –exlcude-metadata flag with the restic diff command.

# Incorrect advice provided by DeepSeek
restic diff --exclude-metadata <snapshot1> <snapshot2>

Because I’ve already bumped my head on this question, I identified the error: Restic has no such parameter.

DeepSeek had conflated Restic’s behavior with other tools (e.g., rsync or git diff), generating a command that felt logical but didn’t exist.

Why This Happened

1. Pattern Recognition Gone Wrong: Models like DeepSeek predict text based on patterns in training data. The phrase “ignore metadata” likely triggered associations with flags from similar tools, leading to a confident but false answer.
2. Limitations in Training Data: If Restic’s documentation wasn’t prominently represented in the training corpus, the model “improvised” using knowledge of analogous systems.
3. Overconfidence in Plausibility: The hallucinated flag (–exclude-metadata) sounded reasonable, masking its inaccuracy.

What Are AI Hallucinations?

Hallucinations occur when AI generates information that is coherent but factually wrong, unsupported by evidence, or contextually inappropriate. They arise from:

• Data Gaps: The model encounters unfamiliar queries and “fills in” gaps incorrectly.
• Over-Optimization: Prioritizing fluent responses over factual accuracy.
• Ambiguity in Prompts: Poorly phrased requests increase misinterpretation risk.

In technical domains, hallucinations are particularly dangerous. A fabricated command (e.g., rm -rf / –no-configm) could destroy data, while a false API parameter might crash applications.

Why Hallucinations Matter in Technical Support

1. Erosion of Trust: Users may lose confidence in AI tools after encountering errors.
2. Operational Risks: Incorrect commands can disrupt systems, corrupt data, or create security vulnerabilities.
3. Time Wastage: Engineers might debug nonexistent issues caused by faulty AI advice.

In the Restic example, the hallucination was relatively benign, but it underscores the need for vigilance.

Mitigating Hallucinations: Strategies for Users and Developers

For Users

1. Verify Against Official Docs: Always cross-check AI suggestions with authoritative sources (e.g., Restic’s documentation.)
2. Use Sandboxes: Test commands in safe environments (e.g., Docker containers) before running them in production.
3. Provide Feedback: Report errors to AI developers. In our case, the user’s correction improved DeepSeek’s knowledge.

For AI Developers

1. Improve Fact-Checking: Integrate real-time validation against trusted databases or APIs.
2. Enhance Transparency: Flag low-confidence responses (e.g., “This flag is unverified for Restic”).
3. Curate Technical Training Data: Prioritize accuracy in domains like DevOps, where errors have high stakes.

DeepSeek’s Approach to Reducing Hallucinations

DeepSeek employs several strategies to minimize hallucinations:

• Fine-Tuning on Technical Corpora: Training on manuals, forums, and RFCs to anchor responses in factual data.
• User Feedback Loops: Errors like the Restic example are logged to refine future outputs.
• Contextual Awareness: Encouraging users to specify tools and versions (e.g., “Restic 0.16.0 on Linux”) improves precision.

Conclusion: Collaboration Between Humans and AI

The Restic incident highlights both the promise and pitfalls of AI in technical support. While DeepSeek accelerates problem-solving, users must remain critical—treating AI as a collaborator, not an oracle. By combining AI’s speed with human scrutiny, we can harness its potential while mitigating risks.

As AI evolves, so must our strategies for ensuring its reliability.

In the words of cybersecurity experts: “Trust, but verify.”