Chatbots, Virtual Assistants, LLMs, GenAI, Foundation Models & AI Cost Economics | 20+ Questions Answer
Q1. Differentiate between a chatbot and a virtual assistant with suitable business examples.
Answer:
A chatbot is a task-oriented AI system designed to handle specific, repetitive interactions such as answering FAQs, tracking orders, or collecting user information. It typically follows predefined scripts or decision trees and operates through text-based interfaces on websites or apps. Businesses use chatbots to reduce customer support workload and provide 24/7 assistance at low cost.
A virtual assistant is a more advanced AI system capable of understanding context, managing multiple tasks, and interacting through voice or text. It uses natural language processing and integrates with multiple applications. Examples include Siri, Alexa, and Google Assistant, which can set reminders, control smart devices, and perform complex actions.
In business, chatbots are ideal for customer service automation, while virtual assistants function as comprehensive digital companions supporting productivity and personalization.
Q2. Explain the key differences in intelligence between chatbots and virtual assistants.
Answer:
Chatbots generally rely on scripted responses, predefined rules, or simple AI models. Their intelligence is narrow and task-specific, making them efficient for predictable interactions but limited in handling complex queries.
Virtual assistants employ advanced AI techniques such as NLP, contextual understanding, and machine learning. They can interpret intent, manage ambiguity, and learn from user behavior. This allows them to handle multi-step tasks, personalized responses, and voice-based interactions, offering a more human-like experience.
Q3. Discuss the role of Zia as Zoho’s virtual assistant in business productivity.
Answer:
Zia acts as Zoho’s integrated virtual assistant, leveraging data across Zoho applications to enhance business efficiency. It analyzes customer interactions, emails, calls, and website activity to provide actionable insights. Zia recommends optimal times to contact customers, predicts sales trends, and suggests relevant products to customers based on behavior patterns.
Its deep integration with Zoho’s ecosystem reduces setup complexity and improves workflow automation. Additionally, Zoho’s strong focus on data privacy ensures secure handling of sensitive business information, making Zia a trusted enterprise-grade assistant.
Q4. Explain how Zobot enhances customer experience for organizations.
Answer:
Zobot is Zoho SalesIQ’s chatbot-building platform that automates customer interactions across websites and apps. It handles FAQs, qualifies leads, schedules appointments, processes payments, and integrates with internal and third-party systems.
By automating repetitive tasks, Zobot frees human agents to focus on high-value interactions. Its flexible design—ranging from drag-and-drop tools to advanced scripting—allows businesses to tailor chatbots to specific needs. This results in faster responses, improved efficiency, and higher customer satisfaction.
Q5. Why does the cost of LLM usage increase with each query?
Answer:
LLMs charge per query because each request triggers a full inference process. The input text is tokenized and passed through all layers of a massive neural network, requiring billions of computations on GPUs. Each generated token consumes compute, memory, and electricity.
In API-based models, providers bundle infrastructure and operational costs into per-token pricing. Therefore, more queries and longer prompts directly increase costs, resulting in linear cost growth with usage.
Q6. Compare API-based LLM usage with running an in-house LLM.
Answer:
API-based LLMs involve no upfront training cost and are easy to deploy, making them ideal for startups or low-volume applications. However, per-query costs scale linearly and can become expensive at high usage.
In-house LLMs require massive upfront investment for training and infrastructure but offer lower marginal inference costs. Over time, for high-volume usage, self-hosting becomes more economical. The choice depends on scale, budget, and long-term strategy.
Q7. Why is “searching” in an LLM computationally expensive?
Answer:
Unlike traditional search engines that retrieve indexed results, LLMs compute responses from scratch. Every query requires a full forward pass through billions of neural network parameters. Even simple questions demand extensive GPU computation, memory usage, and energy consumption.
This fundamental architectural difference makes LLM “search” far more expensive than keyword-based search systems.
Q8. Differentiate between open-source and proprietary software.
Answer:
Open-source software provides access to source code, allowing users to study, modify, and redistribute it freely. It encourages collaboration, innovation, and faster bug fixes. Examples include Linux and Firefox.
Proprietary software restricts access to source code, is owned by a company, and requires paid licenses. Users face limitations on installation, sharing, and customization. Examples include Windows and Microsoft Office.
Q9. Define a foundation model and explain its significance.
Answer:
A foundation model is a large, general-purpose AI model trained on massive datasets using self-supervised learning. It serves as a base that can be adapted to multiple tasks through fine-tuning or prompting.
Its significance lies in reusability, scalability, and the ability to exhibit emergent capabilities, reducing the need to build task-specific models from scratch.
Q10. Explain the relationship between foundation models and LLMs.
Answer:
LLMs are a subset of foundation models specialized in language tasks. While all LLMs are foundation models, not all foundation models are LLMs. Foundation models may handle text, images, audio, or video, whereas LLMs focus primarily on text generation and understanding.
Q11. Distinguish between LLMs and Generative AI.
Answer:
LLMs are text-focused AI models designed to generate and understand language. Generative AI is a broader category encompassing models that generate text, images, audio, video, and code. LLMs are one type of Generative AI.
Q12. Explain why LLMs are not suitable for quantitative prediction.
Answer:
LLMs predict text tokens, not numeric outcomes based on mathematical relationships. They lack explicit modeling of time-series, causality, and statistical structures. Therefore, while they can explain or assist prediction models, they cannot replace quantitative forecasting methods like ARIMA or regression.
Q13. Describe the role of prediction models in Quantitative Techniques.
Answer:
Prediction models use historical numeric data to estimate future outcomes. They help reduce uncertainty in business, finance, operations, and economics. Common types include time-series, regression, causal, and probabilistic models.
Q14. Compare ARIMA and LLMs in forecasting.
Answer:
ARIMA is a statistical model that produces numeric forecasts using time-series data and explicit parameters. LLMs cannot generate accurate numeric forecasts but can explain results, generate code, and summarize insights. They complement rather than replace ARIMA.
Q15. Explain the concept of hybrid predictive systems.
Answer:
Hybrid systems combine quantitative models for numeric accuracy with LLMs for explanation and automation. Numeric models generate forecasts, while LLMs interpret, summarize, and communicate results, improving decision-making.
Q16. Discuss emergent behaviors in foundation models.
Answer:
Emergent behaviors are abilities that appear unexpectedly as model scale and data increase. Examples include reasoning, abstraction, and creativity. These capabilities are not explicitly programmed but arise from large-scale training.
Q17. What ethical challenges do foundation models pose?
Answer:
Foundation models raise concerns related to bias, privacy, misinformation, copyright, environmental impact, and misuse. Their general-purpose nature amplifies societal impact, requiring strong governance and ethical oversight.
Q18. Explain why LLM outputs lack reproducibility.
Answer:
LLMs use probabilistic sampling controlled by parameters like temperature. This randomness means the same prompt can produce different outputs, which is unsuitable for precise quantitative prediction.
Q19. Why are quantitative models more interpretable than LLMs?
Answer:
Quantitative models have explicit mathematical structures and parameters that can be analyzed and tested statistically. LLMs operate as black boxes with billions of parameters, making interpretation difficult.
Q20. Explain the business value of combining LLMs with GenAI models.
Answer:
Combining LLMs with other GenAI models enables multimodal products that can chat, generate images, process speech, and create videos. This integration improves user experience, productivity, and innovation by offering a unified AI interface.
Q21. Summarize best practices for enterprise AI deployment.
Answer:
Enterprises should use chatbots for repetitive tasks, virtual assistants for complex workflows, quantitative models for prediction, and LLMs for explanation and automation. Hybrid systems deliver optimal accuracy, scalability, and business value.
Chatbot vs Virtual Assistant MCQs, LLM vs GenAI MCQs, Foundation Model MCQs, MBA AI MCQs, AI Cost Economics MCQs, Open Source vs Proprietary Software MCQs, Prediction Models MCQs, Quantitative Techniques AI
