{"id":1650,"date":"2026-04-08T06:10:24","date_gmt":"2026-04-08T06:10:24","guid":{"rendered":"https:\/\/datatype.co.in\/blog\/?p=1650"},"modified":"2026-04-08T06:10:26","modified_gmt":"2026-04-08T06:10:26","slug":"genai-interview-questions","status":"publish","type":"post","link":"https:\/\/datatype.co.in\/blog\/genai-interview-questions\/","title":{"rendered":"GenAI Interview Questions"},"content":{"rendered":"\n

Here is a full Generative AI (GenAI) mock interview<\/strong> with structured questions + strong sample answers<\/strong> that are polished, crisp, and ready for real interviews (Architect, Lead, Senior roles).<\/p>\n\n\n\n

\n\n\n\n

Round 1 \u2014 Fundamentals<\/strong><\/h3>\n\n\n\n

1. What is Generative AI?<\/strong><\/p>\n\n\n\n

Answer:<\/strong>
Generative AI is a class of models that learn from large datasets and generate new content\u2014text, images, code, audio, or video\u2014by predicting the next most likely output. Instead of classifying or detecting patterns like traditional ML, GenAI creates new<\/em> artifacts using models such as transformers and diffusion models.<\/p>\n\n\n\n

\n\n\n\n

2. How do LLMs like GPT work?<\/strong><\/p>\n\n\n\n

Answer:<\/strong>
LLMs are trained on massive text corpora using a transformer-based architecture.
They operate using the following concepts:<\/p>\n\n\n\n

    \n
  • Tokenization<\/strong> \u2013 Breaking text into tokens<\/li>\n\n\n\n
  • Embeddings<\/strong> \u2013 Converting tokens into high\u2011dimensional vectors<\/li>\n\n\n\n
  • Self\u2011Attention<\/strong> \u2013 Model learns relationships between tokens<\/li>\n\n\n\n
  • Decoder-only transformer<\/strong> \u2013 Predicts next token iteratively<\/li>\n\n\n\n
  • Reinforcement Learning from Human Feedback (RLHF)<\/strong> \u2013 Aligns the model with human intent<\/li>\n<\/ul>\n\n\n\n
    \n\n\n\n

    3. What is a Transformer?<\/strong><\/p>\n\n\n\n

    Answer:<\/strong>
    A transformer is a neural network architecture built around self-attention<\/strong>, which allows it to weigh the importance of different parts of the input sequence.
    Key components include:<\/p>\n\n\n\n

      \n
    • Multi-head attention<\/li>\n\n\n\n
    • Positional encoding<\/li>\n\n\n\n
    • Feed-forward networks<\/li>\n\n\n\n
    • Residual connections & layer norm<\/li>\n<\/ul>\n\n\n\n

      Transformers replaced RNNs\/Seq2Seq by enabling parallelization and better long-range pattern learning.<\/p>\n\n\n\n

      \n\n\n\n

      Round 2 \u2014 Intermediate (Architecture, RAG, Vector DBs)<\/strong><\/h3>\n\n\n\n

      4. Explain the architecture of a typical GenAI application.<\/strong><\/p>\n\n\n\n

      Answer:<\/strong>
      A standard GenAI stack has five layers:<\/p>\n\n\n\n

        \n
      1. Model Layer<\/strong>
        GPT, Llama, Claude, Gemini, Stable Diffusion<\/li>\n\n\n\n
      2. Knowledge Layer<\/strong>
        Vector DB (Pinecone, Redis, Chroma), embeddings, retrieval<\/li>\n\n\n\n
      3. Orchestration Layer<\/strong>
        LangChain, Semantic Kernel, DSPy, Azure Prompt Flow<\/li>\n\n\n\n
      4. Application Layer<\/strong>
        Chatbots, copilots, custom enterprise apps<\/li>\n\n\n\n
      5. Governance Layer<\/strong>
        Logging, safety filters, access control, data privacy<\/li>\n<\/ol>\n\n\n\n

        This architecture supports RAG, tool invocation, and custom workflows.<\/p>\n\n\n\n

        \n\n\n\n

        5. What is RAG? Why do we use it?<\/strong><\/p>\n\n\n\n

        Answer:<\/strong>
        RAG (Retrieval-Augmented Generation)<\/strong> combines LLM reasoning with enterprise knowledge.
        Process:<\/p>\n\n\n\n

          \n
        1. User query \u2192 embedding<\/li>\n\n\n\n
        2. Vector DB retrieves relevant documents<\/li>\n\n\n\n
        3. Query + documents passed to LLM<\/li>\n\n\n\n
        4. Model generates grounded output<\/li>\n<\/ol>\n\n\n\n

          Why use it:<\/strong><\/p>\n\n\n\n

            \n
          • Reduces hallucinations<\/li>\n\n\n\n
          • Enables domain-specific knowledge<\/li>\n\n\n\n
          • Keeps data external to the model (no re-training needed)<\/li>\n<\/ul>\n\n\n\n
            \n\n\n\n

            6. What is a vector database and why is it needed?<\/strong><\/p>\n\n\n\n

            Answer:<\/strong>
            A vector DB stores high-dimensional embeddings and performs fast similarity search using ANN (Approximate Nearest Neighbor).
            Used for:<\/p>\n\n\n\n

              \n
            • Context retrieval in RAG<\/li>\n\n\n\n
            • Semantic search<\/li>\n\n\n\n
            • Memory storage for assistants<\/li>\n<\/ul>\n\n\n\n

              Popular options: Pinecone, ChromaDB, Redis, Qdrant, Azure AI Search (vector mode).<\/p>\n\n\n\n

              \n\n\n\n

              Round 3 \u2014 Advanced (Scaling, Fine-Tuning, Safety)<\/strong><\/h3>\n\n\n\n

              7. Difference between RAG and fine-tuning?<\/strong><\/p>\n\n\n\n

              RAG<\/strong><\/p>\n\n\n\n

                \n
              • Adds knowledge externally<\/li>\n\n\n\n
              • No model training<\/li>\n\n\n\n
              • Cheaper, maintainable<\/li>\n\n\n\n
              • Good for facts, documentation<\/li>\n<\/ul>\n\n\n\n

                Fine-Tuning<\/strong><\/p>\n\n\n\n

                  \n
                • Changes model behavior<\/li>\n\n\n\n
                • Good for style, structure, domain nuance<\/li>\n\n\n\n
                • Expensive + needs GPUs<\/li>\n\n\n\n
                • Risk of catastrophic forgetting<\/li>\n<\/ul>\n\n\n\n

                  Often both are combined.<\/p>\n\n\n\n

                  \n\n\n\n

                  8. Explain LoRA\/QLoRA.<\/strong><\/p>\n\n\n\n

                  Answer:<\/strong>
                  LoRA (Low-Rank Adaptation) is a lightweight fine-tuning technique.
                  It freezes the base model and trains small rank-decomposition matrices<\/strong>, making updates efficient.<\/p>\n\n\n\n

                  QLoRA adds 4-bit quantization<\/strong>, allowing fine-tuning large models (33B+) on a single GPU.<\/p>\n\n\n\n

                  \n\n\n\n

                  9. How do you reduce hallucinations?<\/strong><\/p>\n\n\n\n

                  Answer:<\/strong><\/p>\n\n\n\n

                    \n
                  • RAG with well-chunked documents<\/li>\n\n\n\n
                  • Use grounding: citations, references<\/li>\n\n\n\n
                  • Apply system prompts with constraints (\u201cOnly answer using provided context\u201d)<\/li>\n\n\n\n
                  • Use tools\/functions for deterministic tasks<\/li>\n\n\n\n
                  • Use smaller temperature<\/li>\n\n\n\n
                  • Implement enterprise safety\/rule filters<\/li>\n<\/ul>\n\n\n\n
                    \n\n\n\n

                    10. How do you evaluate a GenAI system?<\/strong><\/p>\n\n\n\n

                    Answer:<\/strong><\/p>\n\n\n\n

                    Automatic metrics:<\/strong><\/p>\n\n\n\n

                      \n
                    • BLEU, ROUGE, perplexity<\/li>\n\n\n\n
                    • Embedding similarity<\/li>\n\n\n\n
                    • RAGAS for RAG pipelines<\/li>\n<\/ul>\n\n\n\n

                      Human metrics:<\/strong><\/p>\n\n\n\n

                        \n
                      • Helpfulness, harmlessness, factual accuracy<\/li>\n\n\n\n
                      • Domain expert review<\/li>\n<\/ul>\n\n\n\n

                        Operational metrics:<\/strong><\/p>\n\n\n\n

                          \n
                        • Latency, token cost, retrieval hit rate<\/li>\n\n\n\n
                        • Guardrail violations<\/li>\n<\/ul>\n\n\n\n
                          \n\n\n\n

                          Round 4 \u2014 Scenario-Based Questions<\/strong><\/h3>\n\n\n\n

                          11. Design an enterprise chatbot for internal KB.<\/strong><\/p>\n\n\n\n

                          Answer Outline:<\/strong><\/p>\n\n\n\n

                          Architecture:<\/strong><\/p>\n\n\n\n

                            \n
                          • Frontend \u2192 Web\/Teams\/Slack<\/li>\n\n\n\n
                          • Backend \u2192 Orchestration framework (LangChain\/Semantic Kernel)<\/li>\n\n\n\n
                          • RAG \u2192 Azure Search \/ Pinecone<\/li>\n\n\n\n
                          • LLM \u2192 Azure OpenAI GPT\u20114o or Llama 3<\/li>\n\n\n\n
                          • Memory \u2192 Vector store<\/li>\n\n\n\n
                          • Governance \u2192 Prompt shields, audit logs, encryption<\/li>\n<\/ul>\n\n\n\n

                            Features:<\/strong><\/p>\n\n\n\n

                              \n
                            • Query understanding<\/li>\n\n\n\n
                            • Grounded answers with citations<\/li>\n\n\n\n
                            • Guardrails for PII leakage<\/li>\n\n\n\n
                            • Role-based access<\/li>\n<\/ul>\n\n\n\n
                              \n\n\n\n

                              12. Your LLM is giving inconsistent answers. What do you do?<\/strong><\/p>\n\n\n\n

                              Answer:<\/strong><\/p>\n\n\n\n

                                \n
                              1. Reduce temperature<\/li>\n\n\n\n
                              2. Improve system prompt<\/li>\n\n\n\n
                              3. Add RAG grounding<\/li>\n\n\n\n
                              4. Preferred: use tools for factual tasks<\/li>\n\n\n\n
                              5. Use Templates \/ Structured Outputs (JSON mode)<\/li>\n\n\n\n
                              6. Evaluate prompt drift using logs<\/li>\n<\/ol>\n\n\n\n
                                \n\n\n\n

                                13. Your RAG system retrieves wrong documents. How do you fix it?<\/strong><\/p>\n\n\n\n

                                Answer:<\/strong><\/p>\n\n\n\n

                                  \n
                                • Adjust embedding model (e.g., switch to text-embedding-3-large)<\/li>\n\n\n\n
                                • Improve chunking strategy<\/li>\n\n\n\n
                                • Reduce irrelevant noise<\/li>\n\n\n\n
                                • Add metadata filtering<\/li>\n\n\n\n
                                • Tune top-k and score thresholds<\/li>\n\n\n\n
                                • Use hybrid search (semantic + keyword)<\/li>\n<\/ul>\n\n\n\n
                                  \n\n\n\n

                                  Round 5 \u2014 Senior\/Architect-Level Deep Dive<\/strong><\/h3>\n\n\n\n

                                  14. Explain end-to-end LLM lifecycle in production.<\/strong><\/p>\n\n\n\n

                                  Answer:<\/strong><\/p>\n\n\n\n

                                    \n
                                  1. Data Preparation<\/strong>
                                    Cleaning, chunking, embeddings, metadata<\/li>\n\n\n\n
                                  2. Model Selection<\/strong>
                                    OpenAI \/ Llama \/ Falcon \/ Mistral<\/li>\n\n\n\n
                                  3. Orchestration Layer<\/strong>
                                    Agents, tools, workflows<\/li>\n\n\n\n
                                  4. Evaluation<\/strong>
                                    RAGAS, human evals, QA pipeline<\/li>\n\n\n\n
                                  5. Deployment<\/strong>
                                    API gateway, autoscaling, caching, low-latency inference<\/li>\n\n\n\n
                                  6. Observability<\/strong>
                                    Metrics: token usage, latency, drift
                                    Traces: OpenTelemetry for LLM calls<\/li>\n\n\n\n
                                  7. Governance<\/strong>
                                    Safety filters, policy enforcement, audits, rate limits<\/li>\n<\/ol>\n\n\n\n
                                    \n\n\n\n

                                    15. How do you choose the right model?<\/strong><\/p>\n\n\n\n

                                    Answer:<\/strong>
                                    Based on:<\/p>\n\n\n\n

                                    Requirement<\/strong><\/td>Best Fit<\/strong><\/td><\/tr><\/thead>
                                    Creativity<\/td>GPT\u20114\/4o, Gemini Ultra<\/td><\/tr>
                                    Enterprise grounding<\/td>Llama 3, GPT\u20114o mini<\/td><\/tr>
                                    Cost-sensitive<\/td>Llama 3 8B\/70B, Mistral<\/td><\/tr>
                                    Vision tasks<\/td>GPT\u20114o, Gemini 2.0, Claude 3 Opus<\/td><\/tr>
                                    Multimodal Apps<\/td>GPT\u20114o, Gemini, Grok<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n

                                    Also consider latency, pricing, compliance, token limits, tool support.<\/p>\n\n\n\n

                                    \n\n\n\n

                                    16. How do you perform cost optimization in LLM systems?<\/strong><\/p>\n\n\n\n

                                    Answer:<\/strong><\/p>\n\n\n\n

                                      \n
                                    • Response truncation (max tokens)<\/li>\n\n\n\n
                                    • Use smaller models for simple tasks<\/li>\n\n\n\n
                                    • Caching embeddings + responses<\/li>\n\n\n\n
                                    • Distillation into smaller local models<\/li>\n\n\n\n
                                    • Optimizing RAG to reduce context size<\/li>\n\n\n\n
                                    • Batch inference for high throughput<\/li>\n<\/ul>\n\n\n\n
                                      \n\n\n\n

                                      17. What is prompt engineering? Give examples.<\/strong><\/p>\n\n\n\n

                                      Answer:<\/strong>
                                      Prompt engineering is designing instructions that guide model behavior.<\/p>\n\n\n\n

                                      Examples:<\/strong><\/p>\n\n\n\n

                                        \n
                                      • Zero-shot: “Summarize this\u2026”<\/em><\/li>\n\n\n\n
                                      • Few-shot: Providing examples<\/li>\n\n\n\n
                                      • Chain-of-thought: Encourage reasoning<\/li>\n\n\n\n
                                      • Role prompting: \u201cYou are a cloud architect\u2026\u201d<\/li>\n\n\n\n
                                      • Tool calling prompts<\/li>\n\n\n\n
                                      • JSON mode prompts for structured output<\/li>\n<\/ul>\n\n\n\n
                                        \n\n\n\n

                                        Round 6 \u2014 Final \u201cBar Raiser\u201d Questions<\/strong><\/h3>\n\n\n\n

                                        18. How would you build a multimodal GenAI system?<\/strong><\/p>\n\n\n\n

                                        Answer:<\/strong><\/p>\n\n\n\n

                                          \n
                                        • Use multimodal models like GPT\u20114o, Gemini, Claude 3<\/li>\n\n\n\n
                                        • Combine text, image, audio embeddings<\/li>\n\n\n\n
                                        • Store multimodal embeddings in vector DB<\/li>\n\n\n\n
                                        • Use modality-specific preprocessing pipelines<\/li>\n\n\n\n
                                        • Implement structured output for downstream tasks<\/li>\n\n\n\n
                                        • Stream outputs for real\u2011time interactions<\/li>\n<\/ul>\n\n\n\n
                                          \n\n\n\n

                                          19. Ethical concerns with GenAI?<\/strong><\/p>\n\n\n\n

                                          Answer:<\/strong><\/p>\n\n\n\n

                                            \n
                                          • Hallucinations<\/li>\n\n\n\n
                                          • Copyright risks<\/li>\n\n\n\n
                                          • Bias amplification<\/li>\n\n\n\n
                                          • Data leakage<\/li>\n\n\n\n
                                          • Harmful content generation<\/li>\n\n\n\n
                                          • Model misuse<\/li>\n<\/ul>\n\n\n\n

                                            Mitigations: guardrails, filters, grounding, policy enforcement, monitoring.<\/p>\n\n\n\n

                                            \n\n\n\n

                                            20. Final: What differentiates a great GenAI solution architect?<\/strong><\/p>\n\n\n\n

                                            Answer:<\/strong><\/p>\n\n\n\n

                                              \n
                                            • Deep understanding of LLM internals<\/li>\n\n\n\n
                                            • Ability to balance latency, cost, and accuracy<\/li>\n\n\n\n
                                            • Knowledge of real-world patterns: RAG, tools, agents<\/li>\n\n\n\n
                                            • Hands-on ability to build POCs rapidly<\/li>\n\n\n\n
                                            • Focus on responsible AI, governance, observability<\/li>\n\n\n\n
                                            • Ability to integrate across cloud, data, AI, and UX layers<\/li>\n<\/ul>\n\n\n\n
                                              \n\n\n\n

                                              Here\u2019s a clean GenAI architecture stack comparison for Azure, AWS, and GCP<\/strong> \u2014 perfect for interviews, solution design, and resume talking points.<\/p>\n\n\n\n

                                              \n\n\n\n

                                              GenAI Architecture Stacks Across Azure, AWS, and GCP<\/strong><\/h3>\n\n\n\n

                                              Below is a layer\u2011by\u2011layer mapping<\/strong> of what each cloud offers for building modern GenAI applications (LLMs, RAG, agents, evaluation, governance, observability).<\/p>\n\n\n\n

                                              \n\n\n\n

                                              1. Model Layer (LLMs, Embeddings, Vision Models, Audio Models)<\/strong><\/p>\n\n\n\n

                                              Azure<\/strong><\/p>\n\n\n\n

                                                \n
                                              • Azure OpenAI Service<\/strong> \u2192 GPT\u20114o, GPT\u20114.1, GPT\u20114 Turbo, embeddings (text-embedding-3-large)<\/li>\n\n\n\n
                                              • Phi-3<\/strong>, Llama 3<\/strong>, Mistral<\/strong>, Jais<\/strong>, others via Azure Model Catalog<\/strong><\/li>\n\n\n\n
                                              • Fully managed, enterprise compliant, private networking (VNet)<\/li>\n<\/ul>\n\n\n\n

                                                AWS<\/strong><\/p>\n\n\n\n

                                                  \n
                                                • Amazon Bedrock<\/strong> \u2192 Claude 3, Llama 3, Amazon Titan, Mistral, Cohere<\/li>\n\n\n\n
                                                • Built\u2011in multi\u2011model marketplace<\/li>\n\n\n\n
                                                • SageMaker JumpStart<\/strong> for custom model deployment and fine-tuning<\/li>\n<\/ul>\n\n\n\n

                                                  GCP<\/strong><\/p>\n\n\n\n

                                                    \n
                                                  • Vertex AI Model Garden<\/strong> \u2192 Gemini 2.0 Ultra\/Pro\/Flash, CodeGemma, Imagen, PaLM<\/li>\n\n\n\n
                                                  • Strongest multimodal support thanks to Gemini<\/li>\n\n\n\n
                                                  • One-click deployment & tuning<\/li>\n<\/ul>\n\n\n\n
                                                    \n\n\n\n

                                                    2. Knowledge Layer (RAG + Vector Databases)<\/strong><\/p>\n\n\n\n

                                                    Azure<\/strong><\/p>\n\n\n\n

                                                      \n
                                                    • Azure AI Search (Vector Search)<\/strong>\n
                                                        \n
                                                      • Hybrid search (semantic + keyword)<\/li>\n\n\n\n
                                                      • Metadata filters, scoring profiles, integrated chunking<\/li>\n<\/ul>\n<\/li>\n\n\n\n
                                                      • Azure Cosmos DB for MongoDB vCore (vector)<\/strong><\/li>\n\n\n\n
                                                      • Redis Enterprise on Azure<\/strong><\/li>\n\n\n\n
                                                      • Blob Storage for document ingestion<\/li>\n<\/ul>\n\n\n\n

                                                        AWS<\/strong><\/p>\n\n\n\n

                                                          \n
                                                        • Amazon OpenSearch (Vector Search)<\/strong><\/li>\n\n\n\n
                                                        • Amazon Aurora with pgvector<\/strong><\/li>\n\n\n\n
                                                        • Amazon RDS pgvector<\/strong><\/li>\n\n\n\n
                                                        • Amazon DynamoDB + memory tables<\/strong><\/li>\n\n\n\n
                                                        • S3<\/strong> for corpus storage<\/li>\n<\/ul>\n\n\n\n

                                                          GCP<\/strong><\/p>\n\n\n\n

                                                            \n
                                                          • Vertex AI Vector Search<\/strong> (fully managed, scalable)<\/li>\n\n\n\n
                                                          • AlloyDB + pgvector<\/strong><\/li>\n\n\n\n
                                                          • BigQuery Vector<\/strong><\/li>\n\n\n\n
                                                          • GCS<\/strong> for corpus ingestion<\/li>\n<\/ul>\n\n\n\n
                                                            \n\n\n\n

                                                            3. Orchestration Layer (Agents, Workflows, Tools, Prompting)<\/strong><\/p>\n\n\n\n

                                                            Azure<\/strong><\/p>\n\n\n\n

                                                              \n
                                                            • Azure AI Studio<\/strong> \u2192 Prompt Flow, Evaluations, Safety<\/li>\n\n\n\n
                                                            • Semantic Kernel<\/strong> (C#, Python)<\/li>\n\n\n\n
                                                            • LangChain + Azure integrations<\/strong><\/li>\n\n\n\n
                                                            • Native Function Calling for Azure OpenAI<\/li>\n<\/ul>\n\n\n\n

                                                              AWS<\/strong><\/p>\n\n\n\n

                                                                \n
                                                              • Amazon Bedrock Agents<\/strong> \u2192 multi-step workflows<\/li>\n\n\n\n
                                                              • LangChain + AWS Lambda\/Bedrock<\/strong><\/li>\n\n\n\n
                                                              • Step Functions<\/strong> for orchestration<\/li>\n\n\n\n
                                                              • SageMaker Pipelines<\/strong> for ML workflows<\/li>\n<\/ul>\n\n\n\n

                                                                GCP<\/strong><\/p>\n\n\n\n

                                                                  \n
                                                                • Vertex AI Agent Builder<\/strong> (Data grounding + tool orchestration)<\/li>\n\n\n\n
                                                                • LangChain + Vertex AI extensions<\/strong><\/li>\n\n\n\n
                                                                • Workflow Orchestration<\/strong> via Cloud Workflows<\/li>\n<\/ul>\n\n\n\n
                                                                  \n\n\n\n

                                                                  4. Application Layer (Chatbots, Copilots, Apps)<\/strong><\/p>\n\n\n\n

                                                                  Azure<\/strong><\/p>\n\n\n\n

                                                                    \n
                                                                  • Web apps (Azure App Service, Static Web Apps)<\/li>\n\n\n\n
                                                                  • Teams Copilot apps<\/strong>, Logic Apps, Power Apps<\/li>\n\n\n\n
                                                                  • Enterprise identity via Azure AD<\/li>\n<\/ul>\n\n\n\n

                                                                    AWS<\/strong><\/p>\n\n\n\n

                                                                      \n
                                                                    • Serverless apps (AWS Lambda + API Gateway)<\/li>\n\n\n\n
                                                                    • Amazon Connect for conversational bots<\/li>\n\n\n\n
                                                                    • Amplify for front-end apps<\/li>\n<\/ul>\n\n\n\n

                                                                      GCP<\/strong><\/p>\n\n\n\n

                                                                        \n
                                                                      • Cloud Run \/ App Engine for app hosting<\/li>\n\n\n\n
                                                                      • DialogFlow CX for conversational apps<\/li>\n\n\n\n
                                                                      • Identity via IAM + Identity-Aware Proxy<\/li>\n<\/ul>\n\n\n\n
                                                                        \n\n\n\n

                                                                        5. Governance, Safety, Observability<\/strong><\/p>\n\n\n\n

                                                                        Azure<\/strong><\/p>\n\n\n\n

                                                                          \n
                                                                        • Content Safety filters<\/li>\n\n\n\n
                                                                        • Prompt shields<\/li>\n\n\n\n
                                                                        • OpenTelemetry integration<\/li>\n\n\n\n
                                                                        • Purview for data governance<\/li>\n\n\n\n
                                                                        • Network isolation + private endpoints<\/li>\n<\/ul>\n\n\n\n

                                                                          AWS<\/strong><\/p>\n\n\n\n

                                                                            \n
                                                                          • Bedrock Guardrails<\/li>\n\n\n\n
                                                                          • CloudWatch + X-Ray for LLM observability<\/li>\n\n\n\n
                                                                          • IAM + KMS for security<\/li>\n\n\n\n
                                                                          • Bedrock evals<\/li>\n<\/ul>\n\n\n\n

                                                                            GCP<\/strong><\/p>\n\n\n\n

                                                                              \n
                                                                            • Vertex AI Safety Filters<\/li>\n\n\n\n
                                                                            • Vertex Evaluation (automatic + manual)<\/li>\n\n\n\n
                                                                            • Cloud Logging + Monitoring<\/li>\n\n\n\n
                                                                            • VPC-SC for secure perimeters<\/li>\n<\/ul>\n\n\n\n
                                                                              \n\n\n\n

                                                                              Side\u2011by\u2011Side Summary Table<\/strong><\/p>\n\n\n\n

                                                                              Layer<\/strong><\/td>Azure<\/strong><\/td>AWS<\/strong><\/td>GCP<\/strong><\/td><\/tr><\/thead>
                                                                              Models<\/strong><\/td>Azure OpenAI, Llama 3, Phi-3<\/td>Bedrock (Claude, Titan, Llama)<\/td>Gemini 2.0, PaLM, Gemma<\/td><\/tr>
                                                                              Vector DB<\/strong><\/td>Azure AI Search, Cosmos, Redis<\/td>OpenSearch, Aurora pgvector<\/td>Vertex Vector Search, AlloyDB<\/td><\/tr>
                                                                              Orchestration<\/strong><\/td>Azure AI Studio, Semantic Kernel<\/td>Bedrock Agents, Step Functions<\/td>Agent Builder, Cloud Workflows<\/td><\/tr>
                                                                              Evaluation<\/strong><\/td>Azure AI Eval<\/td>Bedrock model evals<\/td>Vertex Evaluations<\/td><\/tr>
                                                                              Safety<\/strong><\/td>Azure Content Safety<\/td>Guardrails for Bedrock<\/td>Vertex Safety<\/td><\/tr>
                                                                              App Hosting<\/strong><\/td>App Service, Functions, AKS<\/td>Lambda, ECS, EKS<\/td>Cloud Run, GKE<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n
                                                                              \n\n\n\n

                                                                              Here’s a clear, interview\u2011ready explanation<\/strong> of the difference between GenAI<\/strong>, RAG<\/strong>, Agentic AI<\/strong>, and AI Agents<\/strong>, with simple analogies<\/strong> + practical examples<\/strong> you can reuse in interviews.<\/p>\n\n\n\n

                                                                              \n\n\n\n

                                                                              1. Generative AI (GenAI)<\/strong><\/p>\n\n\n\n

                                                                              Definition<\/strong><\/p>\n\n\n\n

                                                                              GenAI refers to models that generate new content<\/strong> \u2014 text, images, code, audio, or video \u2014 based on patterns learned from large datasets.<\/p>\n\n\n\n

                                                                              What it does<\/strong><\/p>\n\n\n\n

                                                                                \n
                                                                              • Predicts next token<\/strong> (LLMs)<\/li>\n\n\n\n
                                                                              • Generates synthetic images, speech, video<\/strong><\/li>\n\n\n\n
                                                                              • Does not access external knowledge<\/strong> unless provided in the prompt<\/li>\n<\/ul>\n\n\n\n

                                                                                Simple Analogy<\/strong><\/p>\n\n\n\n

                                                                                GenAI is like a highly trained writer<\/strong> who creates content based purely on everything they remember.<\/p>\n\n\n\n

                                                                                Example<\/strong><\/p>\n\n\n\n

                                                                                You ask:<\/p>\n\n\n\n

                                                                                \u201cExplain cloud computing in simple terms.\u201d<\/p>\n\n\n\n

                                                                                GPT\u20114, Llama 3, Gemini, Claude etc. generate the answer from their internal knowledge<\/strong>.<\/p>\n\n\n\n

                                                                                \n\n\n\n

                                                                                2. RAG (Retrieval-Augmented Generation)<\/strong><\/p>\n\n\n\n

                                                                                Definition<\/strong><\/p>\n\n\n\n

                                                                                RAG combines an LLM with an external knowledge base<\/strong> (vector DB + retrieval), allowing the model to answer using up\u2011to\u2011date and specific information<\/strong>.<\/p>\n\n\n\n

                                                                                What it does<\/strong><\/p>\n\n\n\n

                                                                                  \n
                                                                                • Retrieves relevant documents<\/li>\n\n\n\n
                                                                                • Feeds them to the LLM<\/li>\n\n\n\n
                                                                                • LLM generates a grounded (less hallucinated) answer<\/li>\n<\/ul>\n\n\n\n

                                                                                  Simple Analogy<\/strong><\/p>\n\n\n\n

                                                                                  RAG is like a writer who first searches the company\u2019s knowledge base<\/strong> and then writes the answer based on documents.<\/p>\n\n\n\n

                                                                                  Example<\/strong><\/p>\n\n\n\n

                                                                                  You ask:<\/p>\n\n\n\n

                                                                                  \u201cSummarize our company\u2019s 2024 leave policy<\/em>.\u201d<\/p>\n\n\n\n

                                                                                  The LLM doesn\u2019t know this by default.
                                                                                  RAG pipeline retrieves the PDF \u2192 extracts relevant chunks \u2192 LLM summarizes it accurately.<\/p>\n\n\n\n

                                                                                  This is how enterprise copilots work (Azure, AWS, GCP copilots).<\/p>\n\n\n\n

                                                                                  \n\n\n\n

                                                                                  3. Agentic AI<\/strong><\/p>\n\n\n\n

                                                                                  Definition<\/strong><\/p>\n\n\n\n

                                                                                  Agentic AI refers to systems where the model can take actions<\/strong>, reason in multiple steps<\/strong>, use tools<\/strong>, and decide its next step autonomously<\/strong>.<\/p>\n\n\n\n

                                                                                  Agentic = \u201cLLM + reasoning + tools + memory + planning\u201d.<\/p>\n\n\n\n

                                                                                  What it does<\/strong><\/p>\n\n\n\n

                                                                                    \n
                                                                                  • Multi-step planning<\/li>\n\n\n\n
                                                                                  • Tool invocation (SQL tools, search tools, API calls)<\/li>\n\n\n\n
                                                                                  • Task decomposition<\/li>\n\n\n\n
                                                                                  • Self\u2011correction<\/li>\n\n\n\n
                                                                                  • Long-running workflows<\/li>\n<\/ul>\n\n\n\n

                                                                                    Simple Analogy<\/strong><\/p>\n\n\n\n

                                                                                    Agentic AI is like giving the writer the ability to use a calculator<\/strong>, search the internet<\/strong>, query a database<\/strong>, run scripts<\/strong>, and decide what to do next<\/strong>.<\/p>\n\n\n\n

                                                                                    Example<\/strong><\/p>\n\n\n\n

                                                                                    You say:<\/p>\n\n\n\n

                                                                                    \u201cAnalyze last month\u2019s sales from the database and send me a summary email.\u201d<\/p>\n\n\n\n

                                                                                    Agentic AI flow:<\/p>\n\n\n\n

                                                                                      \n
                                                                                    1. Agent plans steps<\/li>\n\n\n\n
                                                                                    2. Calls SQL tool \u2192 fetches data<\/li>\n\n\n\n
                                                                                    3. Summarizes using LLM<\/li>\n\n\n\n
                                                                                    4. Calls email\u2011sending tool<\/li>\n\n\n\n
                                                                                    5. Reports completion<\/li>\n<\/ol>\n\n\n\n

                                                                                      This is the concept behind GPT\u2011o agents, Microsoft AutoGen, OpenAI Swarm, LangChain Agents, AWS Bedrock Agents<\/strong>.<\/p>\n\n\n\n

                                                                                      \n\n\n\n

                                                                                      4. AI Agents<\/strong><\/p>\n\n\n\n

                                                                                      Definition<\/strong><\/p>\n\n\n\n

                                                                                      An AI Agent is the actual implementation\/product<\/strong> built using Agentic AI principles.<\/p>\n\n\n\n

                                                                                      Think of Agentic AI = concept<\/strong>,
                                                                                      and AI Agent = the working system built using that concept<\/strong>.<\/p>\n\n\n\n

                                                                                      What it does<\/strong><\/p>\n\n\n\n

                                                                                      AI Agents have:<\/p>\n\n\n\n

                                                                                        \n
                                                                                      • A goal<\/li>\n\n\n\n
                                                                                      • Tools they can use<\/li>\n\n\n\n
                                                                                      • Ability to plan<\/li>\n\n\n\n
                                                                                      • Memory<\/li>\n\n\n\n
                                                                                      • Ability to take actions autonomously<\/li>\n<\/ul>\n\n\n\n

                                                                                        Simple Analogy<\/strong><\/p>\n\n\n\n

                                                                                        If Agentic AI is the philosophy<\/strong>,
                                                                                        an AI Agent is the employee<\/strong> hired using that philosophy.<\/p>\n\n\n\n

                                                                                        Examples of AI Agents<\/strong><\/p>\n\n\n\n

                                                                                          \n
                                                                                        • A Customer Support Agent<\/strong> that reads RFPs, CRM data, and drafts responses<\/li>\n\n\n\n
                                                                                        • A Code Refactoring Agent<\/strong> that uses repo access + tool invocations<\/li>\n\n\n\n
                                                                                        • A Data Analyst Agent<\/strong> that queries DB, cleans data, visualizes, writes report<\/li>\n\n\n\n
                                                                                        • A Travel Booking Agent<\/strong> that searches flights, books tickets, sends itinerary<\/li>\n<\/ul>\n\n\n\n
                                                                                          \n\n\n\n

                                                                                          Putting It All Together (Super Simple)<\/strong><\/p>\n\n\n\n

                                                                                          Concept<\/strong><\/td>What It Means<\/strong><\/td>Analogy<\/strong><\/td>Example<\/strong><\/td><\/tr><\/thead>
                                                                                          GenAI<\/strong><\/td>Content generation from model knowledge<\/td>Writer<\/td>ChatGPT-style Q&A<\/td><\/tr>
                                                                                          RAG<\/strong><\/td>Generator + external knowledge retrieval<\/td>Writer with a library<\/td>Enterprise chatbot reading manuals<\/td><\/tr>
                                                                                          Agentic AI<\/strong><\/td>Systems where models plan & act<\/td>Writer who can use tools<\/td>LLM querying DB + sending emails<\/td><\/tr>
                                                                                          AI Agent<\/strong><\/td>A deployed agent built with Agentic AI<\/td>The actual trained employee<\/td>Customer support agent app<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n
                                                                                          \n\n\n\n

                                                                                          Real-world Example Combining All 4<\/strong><\/p>\n\n\n\n

                                                                                          Scenario<\/strong><\/p>\n\n\n\n

                                                                                          \u201cCreate a monthly business report.\u201d<\/p>\n\n\n\n

                                                                                          How each technology fits:<\/strong><\/p>\n\n\n\n

                                                                                          GenAI<\/strong><\/p>\n\n\n\n

                                                                                          Writes paragraphs of analysis.<\/p>\n\n\n\n

                                                                                          RAG<\/strong><\/p>\n\n\n\n

                                                                                          Fetches:<\/p>\n\n\n\n

                                                                                            \n
                                                                                          • last month\u2019s sales<\/li>\n\n\n\n
                                                                                          • KPIs from dashboards<\/li>\n\n\n\n
                                                                                          • internal policy info<\/li>\n<\/ul>\n\n\n\n

                                                                                            Agentic AI<\/strong><\/p>\n\n\n\n

                                                                                            Plans and executes steps:<\/p>\n\n\n\n

                                                                                              \n
                                                                                            1. Query DB<\/li>\n\n\n\n
                                                                                            2. Retrieve spreadsheets<\/li>\n\n\n\n
                                                                                            3. Generate charts<\/li>\n\n\n\n
                                                                                            4. Write report<\/li>\n\n\n\n
                                                                                            5. Upload PDF<\/li>\n\n\n\n
                                                                                            6. Send email<\/li>\n<\/ol>\n\n\n\n

                                                                                              AI Agent<\/strong><\/p>\n\n\n\n

                                                                                              The final deployed system doing the above end-to-end daily.<\/p>\n\n\n\n

                                                                                              \n","protected":false},"excerpt":{"rendered":"

                                                                                              Here is a full Generative AI (GenAI) mock interview with structured questions + strong sample answers that are polished, crisp, and ready for real interviews (Architect, Lead, Senior roles). Round [ … ]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[323,322],"tags":[301,295,296,324,325,330,326,328,327,289,329,331],"class_list":["post-1650","post","type-post","status-publish","format-standard","hentry","category-gen-ai","category-interview-questions","tag-agentic-ai","tag-ai-agent","tag-crewai","tag-genai","tag-interview-questions","tag-llamaindex","tag-llm","tag-llm-models","tag-machine-learning","tag-python","tag-rag","tag-vector-databse","list-style-post"],"_links":{"self":[{"href":"https:\/\/datatype.co.in\/blog\/wp-json\/wp\/v2\/posts\/1650","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/datatype.co.in\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/datatype.co.in\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/datatype.co.in\/blog\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/datatype.co.in\/blog\/wp-json\/wp\/v2\/comments?post=1650"}],"version-history":[{"count":4,"href":"https:\/\/datatype.co.in\/blog\/wp-json\/wp\/v2\/posts\/1650\/revisions"}],"predecessor-version":[{"id":1654,"href":"https:\/\/datatype.co.in\/blog\/wp-json\/wp\/v2\/posts\/1650\/revisions\/1654"}],"wp:attachment":[{"href":"https:\/\/datatype.co.in\/blog\/wp-json\/wp\/v2\/media?parent=1650"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/datatype.co.in\/blog\/wp-json\/wp\/v2\/categories?post=1650"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/datatype.co.in\/blog\/wp-json\/wp\/v2\/tags?post=1650"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}