150 Generative AI Interview Questions & Simple Answers
1. What is Generative AI?
Answer:
Generative AI is a type of AI that can create new content.
Step by step:
- We give an instruction called a prompt.
- The AI understands the prompt.
- The model generates an output.
- The output can be text, image, code, audio, or video.
Example: ChatGPT can create an email from a simple instruction.
2. How is Generative AI different from traditional AI?
Answer:
Traditional AI mainly analyzes data or makes predictions. Generative AI creates new content.
Example:
Traditional AI → predicts whether an email is spam.
Generative AI → writes an email response.
3. What is the difference between AI, Machine Learning, Deep Learning, and Generative AI?
Answer:
- AI: The broader field of making machines perform intelligent tasks.
- Machine Learning: A part of AI where systems learn from data.
- Deep Learning: Uses neural networks with many layers.
- Generative AI: Creates new content using learned patterns.
Simple flow:
AI → Machine Learning → Deep Learning → Modern Generative AI applications
4. How does Generative AI work?
Answer:
- The model is trained using large amounts of data.
- The data is converted into patterns the model can learn.
- A user provides a prompt.
- The model processes the prompt.
- It predicts and generates the required output.
5. What types of content can Generative AI generate?
Answer:
Generative AI can create:
- Text
- Images
- Code
- Audio
- Video
- Presentations
- Summaries
Example: ChatGPT generates text, while image-generation models create images.
6. What are some popular Generative AI applications?
Answer:
Popular applications include:
- ChatGPT
- Google Gemini
- Claude
- GitHub Copilot
- AI image generators
- AI video tools
- AI coding assistants
They help with writing, coding, research, design, and automation.
7. What is a Generative AI model?
Answer:
A Generative AI model is a trained machine-learning model that can create new content.
Example:
An LLM can generate text after understanding a user’s prompt.
8. What is a foundation model?
Answer:
A foundation model is a large model trained on a broad range of data.
It can then support many different applications.
Example: An LLM can be adapted for chatbots, summarization, coding, and question answering.
9. What is an AI model?
Answer:
An AI model is a system trained using data to perform a particular task.
It learns patterns from training data and uses those patterns to produce predictions or outputs.
10. What is the difference between a discriminative model and a generative model?
Answer:
A discriminative model mainly predicts or classifies.
A generative model creates new content.
Example:
- Discriminative → Is this email spam?
- Generative → Write a professional email.
11. What are Large Language Models?
Answer:
Large Language Models, or LLMs, are AI models trained on large amounts of text data.
They can understand and generate human-like language.
Examples: GPT models, Gemini models, Claude models, and Llama models.
12. What is an LLM?
Answer:
LLM stands for Large Language Model.
It is designed to understand and generate human language.
Simple process:
Prompt → LLM → Generated response
13. What are some popular LLMs?
Answer:
Examples include:
- GPT models
- Google Gemini
- Anthropic Claude
- Meta Llama
- Mistral models
Different models have different capabilities, costs, and deployment options.
14. What is ChatGPT?
Answer:
ChatGPT is an AI application that uses language models to understand user instructions and generate responses.
It can help with writing, coding, research, summarization, brainstorming, and many other tasks.
15. How does ChatGPT generate responses?
Answer:
- The user enters a prompt.
- The system processes the input.
- The prompt is converted into tokens.
- The model analyzes the context.
- It predicts suitable next tokens.
- The tokens form the final response.
16. What are the major limitations of Generative AI?
Answer:
Major limitations include:
- Hallucinations
- Outdated information
- Bias
- Privacy risks
- Security risks
- Incorrect reasoning
- High computational cost
AI output should be reviewed before important decisions.
17. What are hallucinations in Generative AI?
Answer:
A hallucination occurs when an AI generates information that sounds correct but is actually incorrect or unsupported.
Example:
An AI may create a fake research paper or incorrect source.
18. Why can AI models generate incorrect information?
Answer:
LLMs generate responses based on learned patterns. They do not automatically verify every statement against reality.
Incorrect or incomplete training data can also affect the output.
External retrieval, better prompting, and output verification can reduce this problem.
19. What are common business applications of Generative AI?
Answer:
Businesses use Generative AI for:
- Customer support
- Content creation
- Coding
- Document analysis
- Marketing
- Research
- Summarization
- Workflow automation
20. What skills are required to become a Generative AI professional?
Answer:
Important skills include:
- AI fundamentals
- LLM concepts
- Prompt engineering
- Python
- APIs
- Embeddings
- RAG
- Vector databases
- AI agents
- Project development
The required skills depend on whether the role is technical or non-technical.
21. What is Machine Learning?
Answer:
Machine Learning is a branch of AI where computers learn patterns from data instead of following only fixed rules.
Example:
A model can learn from previous transactions and identify potentially fraudulent transactions.
22. What is Deep Learning?
Answer:
Deep Learning is a part of Machine Learning that uses neural networks with multiple layers.
It is widely used for images, speech, language, and Generative AI.
23. What is the difference between supervised and unsupervised learning?
Answer:
Supervised learning:
The model learns from labeled data.
Example: Emails labeled as spam or not spam.
Unsupervised learning:
The model finds patterns in data without predefined labels.
Example: Grouping customers based on behavior.
24. What is reinforcement learning?
Answer:
Reinforcement learning teaches a system through rewards and penalties.
Process:
Action → Result → Reward/Penalty → Learning
The system learns which actions produce better results.
25. What is reinforcement learning from human feedback?
Answer:
RLHF stands for Reinforcement Learning from Human Feedback.
Humans provide feedback on model responses. The model uses this feedback to improve its behavior.
26. Why is RLHF used in LLMs?
Answer:
RLHF helps make LLM responses more useful and aligned with human preferences.
It can improve:
- Helpfulness
- Relevance
- Safety
- Instruction following
27. What is a neural network?
Answer:
A neural network is a machine-learning model inspired by the structure of the human brain.
It contains connected layers of computational units called neurons.
28. What is a deep neural network?
Answer:
A deep neural network has multiple hidden layers between the input and output.
More layers allow the model to learn complex patterns.
29. What is a transformer model?
Answer:
A Transformer is a neural network architecture designed to process sequences efficiently.
It uses attention mechanisms to understand relationships between different parts of the input.
Transformers are the foundation of many modern LLMs.
30. Why are transformers important for Generative AI?
Answer:
Transformers can understand relationships between tokens and process large amounts of context efficiently.
They support powerful language-generation systems and many modern multimodal models.
31. What is an encoder?
Answer:
An encoder converts input information into a useful internal representation.
It is commonly used for understanding or processing input.
32. What is a decoder?
Answer:
A decoder generates output based on the information it receives.
Many text-generation models use decoder-based architectures.
33. What is the difference between encoder-only, decoder-only, and encoder-decoder models?
Answer:
- Encoder-only: Mainly understands input.
- Decoder-only: Mainly generates output.
- Encoder-decoder: Converts one type of sequence into another.
Example: Translation can use an encoder-decoder approach.
34. Why do modern LLMs commonly use transformer architectures?
Answer:
Transformers handle long-range relationships between tokens effectively.
They also support parallel processing during training, which makes large-scale model training practical.
35. What is next-token prediction?
Answer:
Next-token prediction means predicting what token should come next based on previous tokens.
Example:
“I am going to the…”
The model may predict:
“market”
By repeating this process, the model generates a complete response.
36. What is a Large Language Model?
Answer:
An LLM is a large neural-network model trained on text and other data to understand and generate language.
37. How is an LLM trained?
Answer:
- Collect training data.
- Clean and prepare the data.
- Convert text into tokens.
- Train the model to predict tokens.
- Adjust model parameters.
- Evaluate the model.
- Fine-tune or align it for specific behavior.
38. What is pre-training?
Answer:
Pre-training is the initial large-scale training phase.
The model learns general language patterns and relationships from a large dataset.
39. What is fine-tuning?
Answer:
Fine-tuning means further training an existing model on a specific dataset or task.
It helps adapt the model to a particular use case.
40. What is instruction tuning?
Answer:
Instruction tuning trains a model using examples of instructions and desired responses.
It helps the model follow user instructions more effectively.
41. What is the difference between pre-training and fine-tuning?
Answer:
Pre-training:
Teaches general patterns using very large datasets.
Fine-tuning:
Adapts the existing model to a specific task or behavior.
42. What is tokenization?
Answer:
Tokenization converts text into smaller units called tokens.
A token may represent a word, part of a word, punctuation, or another text unit.
43. What is a token?
Answer:
A token is a unit of information processed by an LLM.
For example, a word may be one token or several tokens depending on the tokenizer.
44. Why do LLMs use tokens instead of complete words?
Answer:
Tokens allow models to handle words, word parts, symbols, and different languages more efficiently.
This creates a flexible vocabulary.
45. What is a vocabulary in an LLM?
Answer:
A vocabulary is the collection of tokens that a tokenizer can recognize.
Each token generally has a unique identifier.
46. What is a context window?
Answer:
A context window is the maximum amount of information a model can process within a request.
It includes relevant input and, depending on the system, generated output.
47. Why is context-window size important?
Answer:
A larger context window allows an application to provide more information to the model.
This is useful for long documents, conversations, and complex tasks.
48. What happens when input exceeds the context window?
Answer:
The system may need to remove, summarize, or split information.
Otherwise, the request may exceed the model’s allowed context.
49. What is an attention mechanism?
Answer:
Attention allows a model to determine which parts of the input are important when processing a particular token.
It helps the model understand relationships between different words or tokens.
50. How does self-attention work?
Answer:
- The input is converted into tokens.
- Each token interacts with other tokens.
- The model calculates their relationships.
- Important tokens receive more attention.
- The model creates a contextual representation.
51. What is multi-head attention?
Answer:
Multi-head attention uses several attention mechanisms at the same time.
Each head can learn different relationships within the input.
52. What is positional encoding?
Answer:
Positional encoding provides information about the position of tokens.
This helps the model understand the order of words in a sequence.
53. What is a transformer architecture?
Answer:
A Transformer architecture uses mechanisms such as attention, feed-forward layers, and positional information to process sequences.
It forms the foundation of many modern language models.
54. What is next-token prediction?
Answer:
It is the process of predicting the most appropriate next token based on previous tokens.
Repeated predictions generate the final response.
55. How does an LLM generate text one token at a time?
Answer:
- The model receives the prompt.
- It calculates probabilities for possible next tokens.
- It selects a token according to the generation settings.
- The selected token becomes part of the context.
- The process repeats until the response ends.
56. What is prompt engineering?
Answer:
Prompt engineering is the process of designing effective instructions for AI models.
The goal is to get useful, accurate, and consistent outputs.
57. Why is prompt engineering important?
Answer:
A clear prompt gives the model better context and instructions.
Good prompts can improve the relevance and structure of responses.
58. What is a prompt?
Answer:
A prompt is the instruction or input given to an AI model.
Example:
“Write a 100-word professional email requesting leave.”
59. What are the main components of a good prompt?
Answer:
A good prompt can include:
- Role
- Task
- Context
- Requirements
- Output format
- Examples
- Constraints
60. What is zero-shot prompting?
Answer:
Zero-shot prompting asks the model to perform a task without giving examples.
Example:
“Classify this review as positive or negative.”
61. What is one-shot prompting?
Answer:
One-shot prompting provides one example before asking the model to perform the task.
62. What is few-shot prompting?
Answer:
Few-shot prompting provides multiple examples.
The model uses those examples to understand the expected pattern.
63. What is chain-of-thought prompting?
Answer:
Chain-of-thought refers to prompting approaches that encourage step-by-step reasoning.
In production systems, developers should follow the model provider’s guidance on exposing or requesting reasoning and should generally ask for concise explanations or final results when appropriate.
64. What is role prompting?
Answer:
Role prompting gives the AI a specific role.
Example:
“You are an experienced financial analyst.”
This can help establish the expected style and perspective.
65. What is instruction prompting?
Answer:
Instruction prompting clearly tells the model what task to perform.
Example:
“Summarize this document in five short points.”
66. What is contextual prompting?
Answer:
Contextual prompting provides background information along with the task.
Example:
Giving a company’s product information before asking the model to write an advertisement.
67. What is structured prompting?
Answer:
Structured prompting organizes instructions into clear sections.
For example:
- Role
- Task
- Context
- Rules
- Output format
This can make complex tasks easier for the model to follow.
68. How can you improve a poor prompt?
Answer:
- Clearly define the task.
- Add relevant context.
- Specify the expected output.
- Add examples if needed.
- Remove unnecessary information.
- Test the prompt.
- Improve it based on results.
69. What is prompt chaining?
Answer:
Prompt chaining breaks a large task into multiple smaller AI calls.
Example:
Research → Summarize → Analyze → Generate report
This can make complex workflows easier to manage.
70. What is prompt injection?
Answer:
Prompt injection is an attack where a user tries to manipulate an AI system into ignoring its intended instructions.
Example:
A user may insert instructions inside a document that tell the AI to reveal hidden information.
71. What is a jailbreak prompt?
Answer:
A jailbreak prompt attempts to bypass an AI system’s safety or behavioral restrictions.
72. How can you protect an AI application from prompt injection?
Answer:
- Treat user input as untrusted.
- Separate instructions from retrieved data.
- Validate inputs.
- Limit tool permissions.
- Apply access controls.
- Monitor suspicious requests.
- Test the application against attacks.
73. What is the difference between system, user, and assistant messages?
Answer:
System: Defines high-level behavior or rules.
User: Provides the user’s request.
Assistant: Provides the model’s response.
These roles help structure conversations.
74. What is temperature in an LLM?
Answer:
Temperature controls how much randomness the model uses during generation.
Lower temperature: More predictable output.
Higher temperature: More varied output.
75. What is the difference between temperature and top-p?
Answer:
Both control generation behavior.
Temperature changes how probabilities are distributed.
Top-p limits token selection to a group of likely tokens whose combined probability reaches a chosen threshold.
76. What is an embedding?
Answer:
An embedding is a numerical representation of information.
Text with similar meaning usually produces vectors that are closer together in embedding space.
77. Why are embeddings important in Generative AI?
Answer:
Embeddings help applications search and compare information based on meaning.
They are commonly used in semantic search and RAG systems.
78. How are text embeddings created?
Answer:
- Text is given to an embedding model.
- The model analyzes the meaning.
- It converts the text into numbers.
- The resulting vector represents the text.
79. What is a vector?
Answer:
A vector is a list of numerical values.
In AI, vectors can represent the meaning or characteristics of text, images, or other information.
80. What is a vector database?
Answer:
A vector database stores and searches vector representations.
It helps applications quickly find information that is semantically similar to a query.
81. Why do LLM applications use vector databases?
Answer:
Vector databases make it easier to retrieve relevant information for an LLM.
They are commonly used in RAG applications.
82. What is semantic search?
Answer:
Semantic search finds information based on meaning rather than only matching exact keywords.
Example:
Searching “how to reduce car fuel consumption” may also find a document about “improving vehicle mileage.”
83. What is the difference between keyword search and semantic search?
Answer:
Keyword search: Looks mainly for matching words.
Semantic search: Looks for similar meaning.
Modern systems can also combine both approaches.
84. What is cosine similarity?
Answer:
Cosine similarity measures how similar two vectors are based on the angle between them.
A higher similarity generally means the vectors are more closely related.
85. How does vector similarity work?
Answer:
- Convert information into vectors.
- Convert the query into a vector.
- Compare the query vector with stored vectors.
- Calculate similarity.
- Return the closest matches.
86. What is similarity search?
Answer:
Similarity search finds items that are closest to a query based on their vector representations.
87. What are some popular vector databases?
Answer:
Examples include:
- Pinecone
- Weaviate
- Milvus
- Qdrant
- Chroma
FAISS is also widely used for vector similarity search.
88. What is Pinecone?
Answer:
Pinecone is a managed vector database platform.
It can store embeddings and perform similarity searches for AI applications.
89. What is FAISS?
Answer:
FAISS stands for Facebook AI Similarity Search.
It is a library designed for efficient similarity search over vectors.
90. What is ChromaDB?
Answer:
Chroma is a vector database commonly used for AI and RAG applications.
It can store embeddings and retrieve relevant information.
91. What is RAG?
Answer:
RAG stands for Retrieval-Augmented Generation.
It combines information retrieval with an LLM.
Simple flow:
Question → Search documents → Retrieve relevant information → Send context to LLM → Generate answer
92. Why is RAG used in Generative AI?
Answer:
RAG allows an LLM to use external or private information.
It can help answer questions about company documents, policies, manuals, or other knowledge sources.
93. How does a RAG system work?
Answer:
- Collect documents.
- Split documents into chunks.
- Create embeddings.
- Store embeddings in a vector database.
- Convert the user’s question into an embedding.
- Retrieve relevant chunks.
- Send the retrieved context to the LLM.
- Generate the final answer.
94. What are the main components of a RAG pipeline?
Answer:
- Documents
- Document loader
- Text splitter
- Embedding model
- Vector database
- Retriever
- LLM
- Prompt
- Evaluation system
95. What is document ingestion?
Answer:
Document ingestion is the process of collecting and preparing documents for an AI system.
It may include loading, cleaning, splitting, embedding, and storing documents.
96. What is document chunking?
Answer:
Chunking means dividing a large document into smaller sections.
These sections are easier to retrieve and send to the LLM.
97. Why is chunking important in RAG?
Answer:
Very large chunks may contain unnecessary information.
Very small chunks may lose important context.
Good chunking helps retrieval find useful information while preserving enough context.
98. What are different chunking strategies?
Answer:
Common approaches include:
- Fixed-size chunking
- Sentence-based chunking
- Paragraph-based chunking
- Semantic chunking
- Recursive chunking
The best approach depends on the document type.
99. What is an embedding model in RAG?
Answer:
An embedding model converts text into numerical vectors.
The system uses these vectors to find relevant document chunks.
100. How does a vector database support RAG?
Answer:
The vector database stores document embeddings.
When a user asks a question, the system searches the database for similar vectors and retrieves relevant information.
101. What is retrieval?
Answer:
Retrieval is the process of finding relevant information from a knowledge source.
In RAG, the retriever finds document chunks related to the user’s question.
102. What is reranking?
Answer:
Reranking takes the initially retrieved results and orders them again based on relevance.
This can improve the quality of the context sent to the LLM.
103. What is the difference between RAG and fine-tuning?
Answer:
RAG: Gives the model relevant external information during the request.
Fine-tuning: Changes the model’s behavior by training it further on specific examples.
104. When should you use RAG instead of fine-tuning?
Answer:
RAG is useful when:
- Information changes frequently.
- You need private documents.
- You want source-based answers.
- You need to update knowledge without retraining the model.
105. What are common problems in RAG systems?
Answer:
Common issues include:
- Poor document chunking
- Bad embeddings
- Incorrect retrieval
- Missing context
- Duplicate information
- Hallucinations
- Poor prompts
- Low-quality source documents
106. What is fine-tuning?
Answer:
Fine-tuning means taking an existing model and training it further using a specific dataset.
It helps adapt the model to a particular task or style.
107. Why do organizations fine-tune an LLM?
Answer:
Organizations may fine-tune a model to improve:
- Task performance
- Response style
- Domain behavior
- Instruction following
- Output formatting
108. When should you fine-tune a model?
Answer:
Consider fine-tuning when the model consistently needs a specific behavior that prompting alone does not provide.
You should first evaluate whether better prompting or RAG can solve the problem.
109. When should you avoid fine-tuning?
Answer:
Avoid unnecessary fine-tuning when:
- Information changes frequently.
- RAG can provide the required knowledge.
- The task can be solved with prompting.
- You do not have enough quality training data.
110. What is supervised fine-tuning?
Answer:
Supervised fine-tuning trains a model using examples containing inputs and desired outputs.
The model learns to produce outputs similar to the provided examples.
111. What is parameter-efficient fine-tuning?
Answer:
Parameter-efficient fine-tuning, or PEFT, updates only a small portion of the model’s parameters.
This reduces memory and computing requirements.
112. What is PEFT?
Answer:
PEFT stands for Parameter-Efficient Fine-Tuning.
It provides methods for adapting large models without updating every parameter.
113. What is LoRA?
Answer:
LoRA stands for Low-Rank Adaptation.
Instead of updating the entire model, LoRA adds smaller trainable components.
This can make fine-tuning more efficient.
114. What is QLoRA?
Answer:
QLoRA combines quantization with LoRA.
It allows large models to be fine-tuned using less memory.
115. What is the difference between LoRA and QLoRA?
Answer:
LoRA: Uses low-rank adapters.
QLoRA: Uses low-rank adapters together with model quantization.
QLoRA can reduce memory requirements further.
116. What is transfer learning?
Answer:
Transfer learning means using knowledge learned from one task or dataset to help solve another related task.
Pretrained AI models are a common example.
117. What is catastrophic forgetting?
Answer:
Catastrophic forgetting occurs when a model loses some previously learned capabilities while learning a new task.
Careful fine-tuning methods can help reduce this problem.
118. What type of dataset is required for fine-tuning?
Answer:
The dataset should be:
- Relevant
- High quality
- Consistent
- Correctly formatted
- Representative of the target task
Poor data can produce poor model behavior.
119. How do you prepare data for fine-tuning?
Answer:
- Define the task.
- Collect relevant examples.
- Clean the data.
- Remove duplicates.
- Format examples correctly.
- Split training and evaluation data.
- Check data quality.
- Run fine-tuning.
- Evaluate the model.
120. How do you evaluate a fine-tuned model?
Answer:
Use a separate evaluation dataset and compare the model’s results against the expected outputs.
Also check:
- Accuracy
- Relevance
- Consistency
- Safety
- Task performance
- Regression against previous capabilities
121. What is an AI agent?
Answer:
An AI agent is a system that can use an AI model to plan or perform tasks using available tools.
Simple flow:
Goal → Reason/Plan → Use tools → Observe results → Continue → Final result
122. How is an AI agent different from a chatbot?
Answer:
A chatbot mainly responds to conversations.
An agent can take actions using tools.
Example:
Chatbot → “Here is your flight information.”
Agent → Searches flights, checks availability, and performs an authorized booking workflow.
123. What are the main components of an AI agent?
Answer:
- AI model
- Instructions
- Tools
- Memory or state
- Planning/workflow
- External data
- Safety controls
124. What is an agentic workflow?
Answer:
An agentic workflow allows an AI system to complete multiple connected steps toward a goal.
Example:
Research → Analyze → Create report → Review → Deliver
125. What is tool calling?
Answer:
Tool calling allows an AI model to request a specific external function.
Example:
The model can request a weather tool to get current weather information.
126. What is function calling?
Answer:
Function calling allows an LLM to produce structured information that an application uses to call a predefined function.
It connects natural-language requests with software actions.
127. Why do AI agents need tools?
Answer:
LLMs alone cannot directly perform every external action.
Tools allow agents to:
- Search information
- Access databases
- Call APIs
- Calculate values
- Send information
- Perform approved actions
128. What is memory in an AI agent?
Answer:
Memory allows an agent system to retain useful information across steps or interactions.
It can help maintain context and user preferences when appropriate.
129. What is short-term memory?
Answer:
Short-term memory stores information needed during the current task or conversation.
Example:
Remembering what the user asked earlier in the same conversation.
130. What is long-term memory?
Answer:
Long-term memory stores information that may be useful across future interactions.
It requires careful privacy and access controls.
131. What is an autonomous AI agent?
Answer:
An autonomous agent can perform multiple steps toward a goal with limited human intervention.
However, important actions should still use appropriate permissions and human oversight.
132. What is multi-agent AI?
Answer:
Multi-agent AI uses multiple specialized agents that work together.
Example:
Research Agent → Analysis Agent → Writing Agent → Review Agent
133. How can multiple AI agents work together?
Answer:
- Define a main goal.
- Assign different tasks.
- Give each agent suitable tools.
- Pass results between agents.
- Combine the outputs.
- Review the final result.
134. What are the challenges of building AI agents?
Answer:
Challenges include:
- Incorrect actions
- Hallucinations
- Tool failures
- Security risks
- High costs
- Latency
- Poor planning
- Unexpected behavior
135. What are common real-world applications of AI agents?
Answer:
Examples include:
- Customer support
- Research assistants
- Coding assistants
- IT support
- Business automation
- Data analysis
- Workflow automation
136. What is an AI API?
Answer:
An AI API allows an application to communicate with an AI model through software requests.
Example:
Application → API → AI model → Response → Application
137. How do you integrate an LLM API into an application?
Answer:
- Select a model provider.
- Create API credentials.
- Install the required SDK or use HTTP requests.
- Send the prompt and required parameters.
- Receive the model response.
- Process the response.
- Display or use the result.
138. What is an API key?
Answer:
An API key is a credential used to authenticate an application with an API service.
It should be treated as sensitive information.
139. How should API keys be protected?
Answer:
- Never expose them in frontend code.
- Store them in environment variables or a secret manager.
- Rotate compromised keys.
- Use access restrictions where available.
- Monitor API usage.
140. What is the OpenAI API?
Answer:
The OpenAI API allows developers to integrate OpenAI models and capabilities into applications.
Developers can use it for tasks such as text generation, structured outputs, tool calling, and other supported AI capabilities.
141. What is model inference?
Answer:
Inference is the process of using a trained model to generate a result.
Example:
Prompt → Trained model → Generated answer
Training teaches the model. Inference uses the trained model.
142. What is latency in an AI application?
Answer:
Latency is the time taken to receive a response.
Lower latency generally provides a faster user experience.
143. What is token usage?
Answer:
Token usage measures how many tokens an AI application sends to and receives from a model.
Token usage can affect cost and context-window limits.
144. How can you reduce LLM API costs?
Answer:
- Reduce unnecessary prompt content.
- Use appropriate models.
- Limit unnecessary output length.
- Cache repeated requests.
- Use retrieval efficiently.
- Monitor token usage.
- Batch suitable workloads.
- Avoid unnecessary model calls.
145. How would you deploy a Generative AI application to production?
Answer:
- Build and test the application.
- Evaluate model quality.
- Secure API credentials.
- Add authentication and authorization.
- Add logging and monitoring.
- Protect user data.
- Test for security risks.
- Deploy the application.
- Monitor cost, latency, and quality.
- Continuously improve the system.
146. Your RAG chatbot gives incorrect answers even though the information exists in the documents. How would you troubleshoot it?
Answer:
I would troubleshoot the RAG pipeline step by step.
Step 1: Check whether the document was successfully ingested.
Step 2: Check the chunking strategy.
Step 3: Check the embedding model.
Step 4: Test whether the correct chunks are retrieved.
Step 5: Check the similarity threshold and top-k value.
Step 6: Check whether reranking could improve retrieval.
Step 7: Review the prompt sent to the LLM.
Step 8: Check whether the model is using the retrieved context.
Step 9: Evaluate the final answer against the source document.
This helps identify whether the problem comes from ingestion, retrieval, prompting, or generation.
147. Your LLM application produces different answers for the same prompt. What could cause this?
Answer:
Several factors can cause variation.
- Temperature may be higher.
- Sampling settings may allow different outputs.
- The model may be nondeterministic.
- Hidden context may differ.
- Conversation history may differ.
- The model or system configuration may have changed.
I would first check the request parameters and application logs.
148. An AI chatbot is revealing information from another user’s conversation. How would you investigate and prevent this?
Answer:
This is primarily a security and data-isolation issue.
Step 1: Check whether conversation data is properly separated by user.
Step 2: Review authentication and authorization.
Step 3: Check database queries.
Step 4: Review session and memory handling.
Step 5: Check whether retrieved documents contain another user’s data.
Step 6: Test with multiple isolated accounts.
Step 7: Remove unnecessary sensitive information from prompts and logs.
Step 8: Add strict access controls and monitoring.
149. Your company’s LLM API costs suddenly increase by 50%. What would you do?
Answer:
I would investigate the cost increase systematically.
Step 1: Check token usage.
Step 2: Compare current usage with previous usage.
Step 3: Identify which application or users caused the increase.
Step 4: Check whether prompts became larger.
Step 5: Check whether output lengths increased.
Step 6: Look for unnecessary repeated API calls.
Step 7: Review model selection.
Step 8: Add caching or request optimization where appropriate.
Step 9: Set usage limits and monitoring.
150. You are asked to build an AI assistant that answers questions from 10,000 company documents.
Answer:
I would use a RAG-based architecture.
Step 1: Collect documents
Collect the company’s approved documents from the required sources.
Step 2: Process documents
Extract text and clean the documents.
Step 3: Chunk documents
Divide large documents into meaningful chunks while preserving context.
Step 4: Create embeddings
Use an embedding model to convert each chunk into a vector.
Step 5: Store vectors
Store the vectors and document metadata in a vector database.
Step 6: Receive the user question
The user asks a question through the chatbot.
Step 7: Create the query embedding
Convert the question into a vector.
Step 8: Retrieve relevant information
Search the vector database for relevant chunks.
Step 9: Rerank results
Improve the order of retrieved results if needed.
Step 10: Send context to the LLM
Provide the question and relevant document context to the language model.
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