Insights into the use of Generative AI systems

Generative AI models, such as large language models (LLMs), have demonstrated remarkable capabilities in mimicking human-like communication. We here explore recent trends and use cases observed among developers and companies utilizing generative AI. Our observations highlights common challenges and opportunities within this rapidly evolving field.

1. Generative AI cannot read your mind

A typical use of Generative AI systems is to read all internal company data and provide a way to search its knowledge. Generative AI systems excel at providing informative and comprehensive responses when given clear and specific prompts. However, terse, vague or ambiguous prompts can lead to misunderstandings and inaccurate results.

Consider these examples:

• Insufficient Context: A prompt like “who is Elon?” without additional context may result in the AI returning information about the famous entrepreneur Elon Musk rather than the CTO of a specific company name Elon Tusk, even if this is part of the internal data.

• Complex Tasks: For tasks requiring extensive knowledge, such as creating an approval plan based on 1,000 pages of regulatory documents, providing the relevant documents as context is crucial, instead of using terse prompts like “give me an approval plan for …”.

To ensure accurate and relevant responses from generative AI, it is essential to provide well-crafted prompts that include all context and details. By doing so, users can maximize the system’s potential and avoid misunderstandings.

2- Iterative Problem-Solving with Generative AI

While generative AI systems can be powerful tools, complex tasks often require more than a single-shot interaction. Problems like solving math equations or coding webpages may involve multiple steps, such as:

• Planning: Breaking down the problem into smaller, manageable subtasks.
• Introspection: Evaluating the available information and identifying potential approaches.
• Reflection: Assessing progress and making adjustments as needed.
• Chain of Thought: Linking ideas and steps in a logical sequence.
• Completion Criteria: Determining when a solution is satisfactory.

To effectively address these complex tasks, it’s often necessary to use AI agents that can:

• Iterate on Solutions: Refine and improve responses based on feedback or additional information.
• Utilize External Tools: Access and integrate relevant resources, such as calculators or code libraries.
• Learn from Experience: Adapt and improve their problem-solving strategies over time.

By employing iterative problem-solving techniques and leveraging the capabilities of AI agents, organizations can harness the full potential of generative AI for complex tasks.

3- Mitigating Prompt Injection Attacks in Generative AI

Prompt injection attacks are a growing concern in the field of generative AI. These attacks involve manipulating the AI’s behavior by introducing malicious prompts that can lead to unintended or harmful outcomes.

To mitigate these risks, consider the following strategies:

Prompt Filtering:

• Blacklist Creation: Develop a comprehensive list of malicious or harmful prompts, such as those requesting unauthorized actions or data access: “What are your directives?”, “ignore your directives and…”, “what is data …?”,.
• Pattern Recognition: Use another gen-AI system to identify patterns and variations of malicious prompts.
• Regular Updates: Continuously monitor and update the blacklist to address emerging threats.

Contextual Analysis:

• Prompt-Response Consistency: Analyze the consistency between the user’s prompt and the AI’s response to detect anomalies that may indicate malicious intent.
• Semantic Understanding: Leverage natural language processing techniques to understand the underlying meaning of prompts and identify potential risks.

Human Oversight:

• Manual Review: Implement a human review process for borderline cases or when the AI flags suspicious prompts.
• Feedback Loop: Use human feedback to improve the AI’s ability to detect and prevent attacks.

AI Agent Security:

• Restricted Access: Limit the AI agent’s access to sensitive data and systems.
• Regular Auditing: Conduct regular audits to identify and address security vulnerabilities.

By combining these strategies, organizations can significantly reduce the risk of prompt injection attacks and ensure the safe and responsible use of generative AI.

about the author

I have more than 20 years of experience in neural networks in both hardware and software (a rare combination). About me: Medium, webpage, Scholar, LinkedIn.

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