Hasura hosted this wonderful podcast at Supergraph. Get insights into how Generative AI is bringing a paradigm shift in data access and security patterns. We also talk about autonomous agents (this was recorded before OpenAI Dev Day, where they introduced Assistants). Use this blog as an augmented transcript for deeper understanding.
Can you start by providing a brief overview of Generative AI? What are large language models (LLMs)?
Generative AI refers to artificial intelligence models capable of creating new content that is believably human. This content could range from texts, images, voice, animation to code, essentially any output that requires a certain level of creativity.
A large language model is a type of machine-learning model that has been trained on a broad dataset to generate human-like text.
They're trained on a massive corpus of data, and they use that knowledge to produce text that's contextually related to the input they're given.
An example of LLM is GPT-3.5, a language model developed by OpenAI that can generate human-like text based on the input it is given. GPT-3.5 can answer questions, write essays, summarize text, translate languages, and even create poetry.
However, their output can be unpredictable, and they require careful handling to ensure they produce desirable results. They work within a probabilistic framework, meaning the same input may yield different outputs at different times.
Thanks for the intro on GenAI. What kind of use cases/tasks can be automated using LLMs? How can companies leverage the power of generalized Large Language Models for their use case?
Automating Routine Tasks: Large Language Models (LLMs) like ChatGPT can be used to automate various routine tasks in different domains, from auto-generating code to summarizing engineering artifacts.
Chatbot Deployment: LLMs can enhance and automate the functionality of chatbots, allowing for more dynamic and intelligent interactions with customers and internal employees across fields like customer service, HR, marketing, and the developer community.
Content Generation and Enhancement: Large LLMs can also aid with tasks requiring creativity, such as content generation and enhancement, with applications ranging from crafting creative content like poems, songs, or marketing copy to offering language translation and improvement services.
While LLMs can automate these tasks to a certain degree, human intervention is often still needed for refinement, supervision, and alignment with specific strategic goals or unique user requirements.
AI Strategy: To effectively implement an AI strategy, organizations can prioritize specific use cases where LLMs can add the most value. They must also acquire and curate a high-quality dataset, ensure reliable infrastructure, secure stakeholder buy-in, and regularly monitor and update the AI implementation.
Adapting to AI Development: It's crucial for organizations to adapt to the changes brought by AI. This involves fostering a culture of continuous learning and experimentation, encouraging cross-disciplinary understanding and collaboration, and placing higher emphasis on ethics, bias, security, and privacy considerations.
Can you share some of the key obstacles encountered during your journey in Generative AI product development?
High Expectations Vs Reality: One of the key challenges experienced was navigating the gap between high expectations and the reality of AI implementation.
Underestimation of Human Alignment: There was an oversight on the importance of human alignment in AI applications. Even with the most accurate AI solution, adoption failed if users did not find it useful. This was a clear indicator that deliberate efforts were required to drive alignment on a use-case-by-use-case basis.
Ethical and Security Considerations: As Generative AI brings new areas for potential security breaches and ethical concerns, the need for careful assessment and management of these aspects presents a significant challenge.
Balancing Innovation and Tradition: A major hurdle was balancing the innovative capabilities of AI with maintaining the consistency and reliability of traditional software development practices. This required regular testing, consultation, and gradual adjustment of the development process.
Stakeholder Management: Securing buy-in from stakeholders was a challenge due to the significant upfront investment and uncertainty around return on investment (ROI). Constant communication about the progress and potential of AI projects was required to address this hurdle.
Evolving Foundations: Despite heavy investments in AI, like our XP platform, infrastructure and technological limitations posed frequent obstacles to the seamless integration of AI into daily operations. Adopting new tools like Langchain, Llamaindex, and others was crucial for innovation at the edge.
AI's "Probabilistic" Nature: Training and tuning AI to get consistent outputs was found to be nontrivial, as the nature of AI is probabilistic and outputs can vary a lot. This required a delicate balance of art and science.
AI Development Pace: Keeping up with the rapid advancements in the AI domain posed its own set of challenges. It demanded substantial resource commitment for stages like continuous learning, experimentation, adaptation, results monitoring, and improvements.
Undoubtedly, these complexities called for innovative solutions. How did you tackle these challenges?
Prioritize and Define Clear Goals: One key solution was focusing on specific use cases where AI could deliver maximum benefits and setting realistic goals, ensuring alignment with overall strategic objectives.
Invest in Infrastructure: Regular upgrades were made to ensure our tools and infrastructure were ready to adopt and make the most out of the latest advancements in AI, including utilizing emerging tools like Langchain, Llamaindex, and others.
Continuous Communication with Stakeholders: Regular updates and thorough communication with stakeholders about the progress, expectations, and the potential of AI projects helped in managing expectations and securing their continuous support.
Regular Monitoring and Updates: We dedicated resources to track the performance and issues of our AI applications, implementing swift changes when required to ensure the efficiency and relevance of our AI offerings.
Responsible AI Design: We emphasized the ethical implications of AI and incorporated considerations around ethical use, potential bias, data privacy, and fairness into our design and decision-making processes.
Culture of Continuous Learning: Given the rapidly evolving nature of AI, creating a culture of continuous learning and adaptation, and encouraging the team to experiment with new technologies through pilot projects, helped keep pace with AI advancements.
Human-AI Training and Alignment: To tackle underestimating human alignment, we placed more focus on understanding end-user needs and perceptions. We placed emphasis on training AI systems to work in harmony with human behaviors, expectations, and preferences, and validated the usefulness of AI applications through intensive user testing and feedback.
Effective Prompt Engineering: To overcome the challenge of prompt engineering, we encouraged a rigorous approach to testing, learning, and refining AI prompts, helping improve the interactions and outputs of AI applications. This often involved continuous AI training sessions and regular updates to ensure optimal performance.
You mentioned prompt engineering with a lot of stress. How should one think about generating prompts?
Trial and Experimentation: The creation of prompts often requires a significant amount of trial and error. It's valuable to iterate and experiment, observing how different prompts produce varied results, and refining according to desired outcomes.
Understand Target Audience & Objectives: It's essential to understand the exact need or problem that the prompt is intended to solve. Think from the perspective of your end user or target audience. A well-crafted prompt is designed with user needs and objectives in mind.
Blend of Art and Science: Crafting effective AI prompts is both an art and a science. It involves creativity in generating engaging instructions, and a technical understanding of how the AI interprets and responds to these instructions.
Use of Precise Language: Clarity and specificity in language are key. An effective prompt clearly and specifically articulates what you want the AI to do.
Balance Instruction and Exploration: The prompt should strike a balance between giving explicit instructions to the AI and allowing it room to interpret and generate unique responses.
Regular Refinement: Over time, based on feedback and results, it's crucial to refine and optimize prompts to improve their efficiency and effectiveness.
Does the concept of “Garbage in, Garbage out” apply in the context of prompt engineering as well?
Indeed, the concept of "Garbage in, Garbage out" holds for prompt engineering as well. - If the prompt is poorly constructed or the data fed into the model is of low-quality, the output generated will likely be substandard or irrelevant.
Good data and well-crafted prompts often result in better responses from the LLM. The LLMs themselves are tools and they can be quite “buggy” depending on what data it was trained on.
Prompts are the mechanisms to extract the “right” information from the models, but if the models are “garbage” - no prompts can save you!
There is a lot of buzz around autonomous agents. How are agents different from LLM running on engineered prompts?
"Agent" in AI generally refers to an autonomous program capable of observing its environment and acting toward achieving its goal. An agent can be an intelligent machine, software, or a person. The agent's actions or decisions are guided by some form of AI model that has been trained with a specific goal in mind.
With properly engineered prompts, LLMs can function like a simple rule-based agent where they respond by generating text-based output upon receiving an input prompt. However, their learning ends after being trained, and they don't have interactive and adaptive capabilities.
In contrast, an AI Agent operates in an environment where it continuously learns, interacts, and adapts, making it suitable for more complex and dynamic situations.
You spoke about two different approaches to automate tasks with LLMs.
1) Prompt engineering with specific instruction and data/context fetched.
2) Autonomous agent, which can generate queries to fetch information and call required tools to execute actions.
Are we looking at a paradigm shift in data access and security patterns, as we are now relying on new data sources like vector databases and more autonomous access to data? And, what does that new paradigm of data access look like?
Certainly. As Generative AI and autonomous agents require more direct and broader access to databases and other data sources, there's a fundamental shift in data access and security patterns.
In lieu of traditional data access methods, we see the rise of novel tools such as vector databases, capable of storing, sharing, and manipulating large amounts of high-dimensional vectors which are used in AI and ML algorithms. This can improve the speed, efficiency, and capacity of data retrieval, but also demands new security measures.
The shift towards more autonomous access to data is enabling AI models to interact with data dynamically. This includes accessing data when needed, processing it in real-time, and even learning from it continuously. This, too, calls for rigorous security controls.
Therefore, this new paradigm demands robust and flexible data governance frameworks. Solutions can range from establishing strict access controls, employing data anonymization techniques to preserve privacy, developing real-time monitoring systems to detect and mitigate potential threats, to designing efficient alert and response mechanisms.
Moreover, there's also an emphasis on transparent data practices: clear tracking of who accesses data, when, and for what purposes. This not only increases accountability but can also aid in auditing and troubleshooting.
Lastly, the use of security measures that align with AI mechanisms, like differential privacy and federated learning, are strategies being adopted to ensure protection while allowing necessary access for AI functionalities.
A point to note is the need for an ethos of ethical data usage. As we provide AI with increased access to data, the gravity of ethical and responsible usage of data rises. It's important to remember the impact these models have - both intended and unintended - on individuals and communities that the data represents.
To sum up, the new data access paradigm in the AI-first era is about finding the fine balance between facilitating the dynamic, autonomous data access the AI requires and ensuring robust data security and privacy practices. Teams and organizations must embrace this duality to succeed in the era of Generative AI.
What an interesting time to be living in.
Thank you, Bhavesh, for the enlightening conversation! Do you have any final thoughts or advice for those venturing into this exciting field of Generative AI?
Embrace the Shift: We are indeed witnessing a monumental shift, thanks to Generative AI. As we transition towards AI-driven processes, it's essential to stay open-minded, embrace change, and be willing to experiment and adapt.
Continuous Learning: The pace of AI advancement calls for continuous learning. Stay updated with the latest AI research, applications, and case studies. Online resources, academic papers, newsletters, and forums are excellent platforms for this.
Practical Experience: Gaining hands-on experience is critical. Participate in hackathons, create prototypes, and use open-source AI models and tools to get practical experience.
Responsible AI: Always keep ethical aspects of AI in mind: privacy, security, fairness, and transparency. As AI becomes more prominent, the ethical implications of AI deployment become increasingly critical.
Collaborative Approach: AI is highly interdisciplinary, involving stakeholders from varying domains. The key to successful AI deployment lies in effective collaboration between domain experts, data scientists, software engineers, product managers, and more.
Patience and Perseverance: Lastly, be patient. Mastery of AI is a marathon, not a sprint. There will be setbacks, but with curiosity, perseverance, and a problem-solving mindset, the obstacles become learning opportunities.
~10xManager