In today's fast-paced digital landscape, AI isn't just a tool for generating content - it can also help you fine-tune your prompts themselves for better results.  By using AI to analyze and refine your queries, you will recieve clearer, more creative and targeted responses.  If you're counting tokens, it will also save on usage costs.

So try asking your AI system of choice:

"Please optimise the following prompt and add placeholders for any additional information I might need to provide:  

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Please write me a process to deploy nginx as a docker image to act as a reverse proxy for a web server

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Different AI systems will definitely yield varying results, so try posting this on a few.  This helps in so many ways to organise your thoughts and information gathering, its a real time saver, especially when you run the resultant prompt!  

We've almost stopped using search engines in favour of well written prompts.  Integrating AI into your prompt-writing process is a smart way to save even more time fault finding and prompt refining...

Happy optimizing!

ps: Please pardon my manners when writing prompts, you really don't need to do that, but do read our other blog posts about prompt manners!

DeepSeek R1 is a cutting-edge AI model known for its reasoning capabilities, developed by DeepSeek, a Chinese AI research lab. It's designed for tasks ranging from data exploration to complex problem-solving.

Ollama, on the other hand, is a platform that allows users to run large language models like DeepSeek R1 on local machines, enhancing data privacy and reducing dependency on cloud services. While this setup offers numerous benefits, including lower latency and cost efficiency, it's not without risks.

1. Data Poisoning:
   • Risk: Models like DeepSeek R1 can be manipulated through data poisoning, where malicious actors introduce corrupt data into the training set. This could lead to the model producing harmful or incorrect outputs inadvertently.
   • Mitigation: Regularly audit and update the training data. Use verification tools to check the integrity of the data before and after model updates.
2. Model Jailbreaking:
   • Risk: There have been reports of security vulnerabilities where DeepSeek R1 could be tricked into generating harmful content, like ransomware code or instructions for dangerous activities. This vulnerability can be more pronounced when running models locally without regular security patches or updates.
   • Mitigation: Stay informed about known vulnerabilities. Implement additional layers of security checks or filters on model outputs. Regularly update the model or use patches as they become available from the developers.
3. Privacy and Data Exposure:
   • Risk: While running DeepSeek R1 locally reduces data transmission to third parties, there's still a risk if the model or its outputs are not properly secured. Malware or unauthorized access to the local system could expose sensitive data processed by the model
   • Mitigation: Ensure robust local security measures like strong antivirus software, firewalls and access controls. Encrypt sensitive data before processing and be cautious about what data you feed into the model.
4. Virus or Malware from Model Execution:
   • Risk: Some posts on social platforms have raised concerns about AI models potentially carrying viruses or other forms of malware, although the likelihood is debated. The risk is more significant with models from less scrutinized sources
   • Mitigation: Download models only from reputable sources. Use sandbox environments for initial testing of new models or updates. Keep your system and all software up to date to protect against known vulnerabilities.
5. Backdoor Threats:
   • Risk: Given the model's origin, there's scepticism regarding potential backdoors, especially in contexts involving sensitive or strategic data processing
   • Mitigation: Use code auditing tools to check for any suspicious code or behavior. Consider using models from different origins or combining models to cross-check outputs.

• Regular Updates: Keep both Ollama and DeepSeek R1 updated to leverage the latest security patches
• Secure Environment: Run the model within a secure, isolated environment or a virtual machine to limit system-wide risks
• Educate Yourself: Understand the model's capabilities and limitations to avoid misuse or misinterpretation of outputs
• Community and Forums: Engage with developer communities for insights on security practices or known issues with the model.

Since I first started using ChatGPT, my prompts have always been courteous "Please can you..." and "Thank you" at the end of the conversation.  A friend commented the other day that this makes no difference whatsoever to the LLM and is really just wearing out my keyboard prematurely.

I take a different view... Just like humans don't take kindly to having orders barked at them, I think AI will start reacting differently to less polite prompts.  I expect that as LLMs become increasingly sophisticated they will be able to pick up a positive or negative stance and their responses will start to vary accordingly. 

Another aspect may be that it is good practise to be polite, and getting in the habit of just giving orders makes us into less tolerable people in daily life, so it's good to keep up those manners!  

Anyway, Thank you for reading!

Hi, and welcome to the renewed Lanboss!  Our previous clients will remember us for our networked systems monitoring, management and security tooling... 

In renewing Lanboss, we're using our data science and anaylsis backgrounds to help organisations prepare datasets for LLM training and fine-tuning, alongside our user education and governance services - all to promote the safe use of AI in the workplace.  We aim to release a number of products this year using AI as well as provisioning AI solutions for our clients.