20 practical, intermediate-level interview questions on Gen AI

Generative AI (Gen AI) is rapidly transforming real-world projects across industries, from automating customer support to enhancing content creation and streamlining business workflows. As organizations adopt AI-powered solutions, the demand for skilled professionals who can design, implement, and optimize Gen AI systems is growing. This article covers 20 practical, intermediate-level interview questions based on real project experiences to help you understand the technical challenges and solutions in Gen AI. Whether you're preparing for interviews or improving your project skills, these insights will strengthen your understanding of modern Gen AI applications.

1. How do you design a Gen AI solution for a customer support chatbot?

Answer: Start by collecting FAQs, support tickets, and chat history to build a relevant dataset. Choose an LLM like GPT-3.5 or Llama-2 for natural conversations. Implement RAG (Retrieval-Augmented Generation) to pull real-time knowledge from internal databases. Fine-tune the model to align with brand tone. Deploy using APIs, manage prompts effectively with frameworks like LangChain, and integrate continuous feedback to retrain based on incorrect or incomplete responses.

2. What is prompt engineering and why is it crucial in Gen AI projects?

Answer: Prompt engineering focuses on designing effective inputs to ensure accurate, relevant outputs from LLMs. In projects, weak prompts lead to off-topic or false answers. For example, in a policy generator, a detailed prompt like "Generate an employee leave policy based on Indian labor law" improves accuracy. Proper prompt structure minimizes model confusion, reduces costs, and boosts performance without modifying the model's core parameters.

3. How do you handle data privacy in Gen AI solutions?

Answer: Identify and classify sensitive information such as PII or PHI. Apply anonymization, encryption, or data masking before processing with third-party APIs. Use private deployment options for models when needed, like running Llama-2 locally. Secure interactions through access controls, audit logs, and secure API gateways to ensure that only authorized users and systems handle sensitive information during AI processing.

4. What is RAG (Retrieval-Augmented Generation) and when should you use it?

Answer: RAG combines LLMs with dynamic data retrieval from external or internal sources to generate context-aware outputs. It's useful when models lack updated information. In practice, documents are embedded into a vector store (like FAISS or Pinecone). At query time, relevant documents are fetched and provided to the LLM, ensuring responses are grounded in up-to-date or proprietary data without full model retraining.

5. Describe how you fine-tune an LLM for domain-specific tasks.

Answer: Collect domain-relevant datasets, clean and preprocess them. Use tools like Hugging Face Transformers and techniques like LoRA for efficient adaptation. Deploy training on GPU-backed infrastructure and validate outputs through expert review. Hyperparameters are adjusted to optimize accuracy, and models are tested against edge cases before integrating into production systems.

6. How do you control hallucinations in Gen AI outputs?

Answer: Reduce hallucinations through precise prompts, limiting model creativity (temperature control), and grounding responses with RAG techniques. Fact-checking outputs post-inference and integrating human reviews in high-risk scenarios are critical. Prompt constraints, such as instructing the model to respond "only from the provided context," are also effective in reducing speculative answers.

7. Explain embedding in Gen AI and its use in real projects.

Answer: Embeddings transform text into numerical vectors that capture semantic meaning. In real projects, embeddings allow for similarity searches across large datasets. For example, customer queries are embedded and matched against stored document embeddings in a vector database to retrieve the most relevant context, improving the LLM's output accuracy and relevance.

8. How do you implement context management in chat-based Gen AI apps?

Answer: Context is managed using techniques like sliding window context (keeping recent interactions), summarization to condense long histories, and storing session data in persistent storage systems like Redis. Frameworks like LangChain help automate context passing and ensure relevant conversation history is maintained across multiple turns.

9. What role does LangChain play in Gen AI architectures?

Answer: LangChain orchestrates multi-step LLM interactions by chaining together components such as document loaders, vector databases, and APIs. It streamlines complex workflows, like querying external sources, embedding documents, and formatting prompts, making the development of production-ready AI applications more manageable.

10. How do you optimize token usage in Gen AI projects?

Answer: Design minimal, clear prompts and provide only the necessary context. Use embedding-based context retrieval to avoid passing large documents directly into the model. Set token limits and monitor usage patterns with analytics tools to identify inefficiencies and reduce unnecessary token consumption.

11. How do you evaluate LLM performance in production?

Answer: Monitor accuracy through test cases, response times, token usage, and user feedback. For text quality, apply metrics like BLEU and ROUGE scores. Analyze conversations to detect failure patterns and apply human reviews for responses flagged as low confidence, adjusting the system based on findings.

12. Explain vector databases in Gen AI workflows.

Answer: Vector databases store and manage text embeddings, enabling similarity searches. When a user submits a query, it is embedded and matched against stored vectors, returning contextually relevant documents. This improves retrieval accuracy and provides high-quality input to the LLM during generation tasks.

13. What’s the difference between fine-tuning and prompt engineering?

Answer: Prompt engineering modifies the input text to guide the model's behavior without altering its weights. Fine-tuning changes the model itself using new training data. Prompt engineering is quick and cost-effective, whereas fine-tuning is resource-intensive but leads to permanent model adaptation for specific tasks.

14. How do you manage model drift in Gen AI projects?

Answer: Track output consistency over time, comparing against benchmarks. Use feedback loops to collect user ratings and examples of degraded performance. Regularly update training data and retrain models when drift is detected to maintain output quality and relevance.

15. What is zero-shot vs few-shot learning in Gen AI?

Answer: Zero-shot learning enables a model to perform tasks without prior examples, relying on general knowledge. Few-shot learning includes sample examples in the prompt to demonstrate task structure. Few-shot is useful when tasks are complex and benefit from pattern reinforcement.

16. Explain chunking in RAG pipelines.

Answer: Chunking breaks large documents into smaller, coherent pieces suitable for embedding and retrieval. Ideal chunk sizes balance meaningful content with model token limits. Overlapping chunks ensure continuity of context between segments, improving accuracy during retrieval.

17. How do you secure API keys in Gen AI applications?

Answer: Store keys securely in environment variables or vault services like AWS Secrets Manager. Avoid hardcoding, enforce least-privilege access, rotate keys regularly, and audit usage to prevent unauthorized access or leaks.

18. What’s the role of caching in Gen AI pipelines?

Answer: Caching stores frequently accessed data like embeddings, search results, or LLM outputs, reducing processing time and API costs. Technologies like Redis or Memcached are used to serve repeated queries efficiently.

19. How do you control the tone and style of LLM outputs?

Answer: Guide the model using explicit instructions in system prompts (e.g., "Respond in a formal tone"). Alternatively, fine-tune the model with a dataset that consistently reflects the desired tone across multiple examples.

20. How do you integrate Gen AI into existing business workflows?

Answer: Use APIs to connect LLMs with business systems like CRMs, ticketing tools, and databases. Workflow automation tools and custom backend services handle data exchange, ensuring Gen AI enhances existing processes without disrupting operations.

Conclusion:

As Generative AI continues to evolve, its role in solving real-world business problems becomes increasingly critical. From building intelligent chatbots to securing sensitive data and optimizing complex workflows, Gen AI demands a deep understanding of both the technology and its practical applications. The interview questions and answers covered in this article reflect the challenges professionals face in actual projects and the strategies needed to address them. By mastering these concepts, you'll not only enhance your technical skills but also position yourself as a valuable contributor in the growing field of AI-driven innovation.