🚀 Generative AI Interview Questions with Answers (Part 6)
2️⃣6️⃣ What is an AI API?
👉 An AI API is an interface that allows an application to send requests to an AI model and receive its output without directly managing the model's internal implementation.
📌 Basic flow:

Application → API Request → AI Model → API Response → Application

Examples of applications:
🔹 Chatbots
🔹 Content Generation
🔹 AI Assistants
🔹 Document Processing
🔹 Code Generation
2️⃣7️⃣ What is Model Serving?
👉 Model Serving is the process of making a trained AI model available for inference so applications can send input and receive predictions or generated outputs.
📌 Typical architecture:

User Request
     ↓
API / Server
     ↓
AI Model
     ↓
Prediction
     ↓
Response

💡 Model serving can be implemented using cloud infrastructure, dedicated servers, or edge devices.
2️⃣8️⃣ What is Semantic Search?
👉 Semantic Search finds information based on the meaning and context of a query, rather than relying only on exact keyword matches.
Example:

Query:
"How can I reset my password?"

May retrieve:
"Steps to recover your account credentials"

💡 Semantic search commonly uses embeddings and vector similarity.
2️⃣9️⃣ What is Vector Search?
👉 Vector Search finds items that are similar in vector space by comparing their embeddings.
📌 Basic flow:

User Query
    ↓
Create Embedding
    ↓
Vector Search
    ↓
Similar Results

Applications:
🔹 RAG Systems
🔹 AI Search
🔹 Recommendation Systems
🔹 Document Retrieval
🔹 Similarity Matching
3️⃣0️⃣ What is an AI Pipeline?
👉 An AI Pipeline is a sequence of connected steps used to process data, run AI models, and produce results.
Example:

User Input
    ↓
Data Processing
    ↓
Embedding / Feature Extraction
    ↓
Model
    ↓
Post-Processing
    ↓
Final Output

A Generative AI pipeline may include:
🔹 Input Validation
🔹 Retrieval
🔹 Prompt Construction
🔹 Model Inference
🔹 Output Validation
🔹 Response Generation
💡 Pipelines help organize complex AI applications into manageable stages.
💬 Save this for your Generative AI interview preparation!
🔥 Next Part will cover 5 questions on LLM Architecture, Self-Attention, Encoder vs Decoder, Pretraining & Inference.
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