🚀 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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AI API vs Traditional REST API: Key Differences
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01. Topic Overview

AI APIs provide artificial intelligence capabilities such as text generation, summarization, classification, and image analysis. Traditional REST APIs allow applications to access resources and perform operations through HTTP methods.

02. AI API

- AI-Powered Chatbots
- Text Generation and Summarization
- Sentiment Analysis and Classification
- Image and Document Analysis
- Speech Recognition and Audio Processing
- Semantic Search and Recommendations

03. Traditional REST API

- Retrieving Product and Customer Information
- Creating and Updating Database Records
- Processing Orders and Booking Requests
- Managing User Accounts and Permissions
- Connecting Payment and Shipping Services
- Integrating Mobile Applications with Backend Systems

04. Key Differences

- Purpose: AI APIs provide AI capabilities, while REST APIs expose resources and business operations.
- Processing: AI APIs use AI models; traditional REST APIs usually follow defined application logic.
- Input: AI APIs may accept text, images, audio, or structured data.
- Output: AI APIs may generate content or predictions, while REST APIs generally return structured data or operation results.
- Predictability: REST API responses are generally more predictable for the same request and data.
- Cost: AI API costs may depend on tokens, model usage, or processed media.

05. Can Both APIs Be Used in One Project?

Yes. REST APIs can manage application data and business operations, while AI APIs provide intelligent features such as conversational recommendations, text summarization, and content analysis.

06. What Should Developers Consider?

- Purpose and Requirements
- Accuracy and Reliability
- Cost and Latency
- Security and Privacy
- Scalability and Maintenance

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READ THE COMPLETE ARTICLE

https://updategadh.com/ai-api-vs-traditional-rest-api/

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