🔹 1.

Agentic AI

 Autonomous systems

 Goal-oriented behavior

 Planning & reasoning

 Multi-agent collaboration

 Reinforcement learning

 Long-term memory

 Tool use (API calling, function chaining)

🔹 2. Prompt Engineering (GenAI)

 Large Language Models (LLMs)

 Token optimization

 Context window

 Few-shot learning

 Chain-of-thought prompting

 Zero-shot prompting

 Instruction tuning

 Prompt injection

 Response grounding

🔹 3. AI / ML / DL

 Artificial Intelligence (AI)

 Machine Learning (ML)

 Deep Learning (DL)

 Neural networks

 Supervised / Unsupervised / Reinforcement learning

 Overfitting / Underfitting

 Backpropagation

 Gradient descent

 Transfer learning

 Hyperparameter tuning
🔹 4. IoT / IIoT

 Edge computing

 MQTT / CoAP / OPC-UA

 Embedded systems

 Sensor nodes

 Digital twin

 SCADA systems

 Predictive maintenance

 LPWAN / NB-IoT

 Real-time telemetry

🔹 5. Sensor Fusion

 Data fusion algorithms

 Kalman filter

 Complementary filter

 Inertial Measurement Unit (IMU)

 Redundancy & noise reduction

 Multimodal sensing

 SLAM (Simultaneous Localization and Mapping)

 Sensor calibration

 Object tracking

🔹 6. Cybersecurity

 Zero Trust Architecture

 Threat modeling

 Identity and Access Management (IAM)

 Encryption (AES, RSA, ECC)

 SIEM (Security Info and Event Management)

 Firewall / IDS / IPS

 Social engineering
  Penetration testing

 Endpoint Detection and Response (EDR)

🔹 7. Blockchain

 Distributed ledger

 Consensus algorithm (PoW, PoS)

 Smart contracts

 Hash functions

 Merkle tree

 Tokenization

 dApps (Decentralized Apps)

 Gas fees

 Forking / Chain splits

🔹 8. Quantum Computing

 Qubit

 Superposition

 Entanglement

 Quantum gate

 Quantum supremacy

 Quantum decoherence

 Quantum circuit

 Shor’s algorithm

 Quantum error correction

 Qiskit / Cirq

🔹 9. DevOps

 CI/CD (Continuous Integration / Continuous Deployment)

 Version control (Git)

 Infrastructure as Code (IaC)

 Containerization (Docker)
  Orchestration (Kubernetes)

 Configuration management (Ansible, Puppet)

 Monitoring and logging

 Blue-Green Deployment

 GitOps

🔹 10. SRE (Site Reliability Engineering)

 SLO (Service Level Objective)

 SLA (Service Level Agreement)

 Error budgets

 Reliability engineering

 Incident management

 Observability (Logs, Metrics, Traces)

 Automation

 Toil reduction

 Chaos engineering

 Blameless postmortem

🔹 11. Semiconductor Manufacturing

 Silicon wafer

 Photolithography

 Doping / Ion implantation

 Etching / Deposition

 CMP (Chemical Mechanical Planarization)

 Cleanroom (Class 10, 100)

 EUV (Extreme Ultraviolet Lithography)

 Yield rate

 Die shrink

 Fab (fabrication facility)

Let me know if you want this turned into a printable cheat sheet or merged with the previous
explanation into a single reference doc.

You said:

club both te content and make it one

ChatGPT said:

Absolutely! Here's a consolidated version that combines the technical explanations, examples, and
key terms into a single, clean, interview-ready document. It’s structured for clarity and depth, with
keywords you can use to demonstrate strong conceptual understanding—whether you're talking to a
CTO or a non-tech stakeholder.

✅ Tech Foundations & Emerging Technologies: Unified Guide with Use Cases & Key Terms

This document serves as a quick but deep-dive explainer across critical modern technology concepts.
Each section includes a short explanation, real-world use cases, and key technical terms to showcase
subject mastery during interviews or cross-functional discussions.

🔹 1. Agentic AI

Explanation: Agentic AI systems demonstrate autonomy, goal-setting, planning, and decision-making

abilities. They go beyond passive models by proactively taking actions in dynamic environments using
tools, memory, and reasoning.

Use Cases:

 AI copilots that perform multi-step tasks (e.g., travel booking or workflow automation)

 AI agents in finance that rebalance portfolios based on market triggers

 Game-playing bots that strategize and adapt (like AlphaStar in StarCraft II)

Key Terms:

 Autonomous agents, multi-step reasoning, tool use (API calling), long-term memory,
reinforcement learning, multi-agent systems

🔹 2. Prompt Engineering (Generative AI)

Explanation: Crafting effective prompts is essential in GenAI systems to control the quality and
relevance of responses from Large Language Models (LLMs). Prompt engineering is the bridge
between user intent and AI behavior.

Use Cases:

 Legal teams summarizing large contracts by prompting LLMs

 HR departments generating job descriptions with tailored tone

  Developers debugging or generating code using structured prompts in tools like GitHub
Copilot

Key Terms:

 LLMs, zero-shot prompting, chain-of-thought, context window, instruction tuning, few-shot

examples, prompt injection

🔹 3. AI / ML / DL

Explanation:

 AI is the overarching field aiming to mimic human intelligence.

 ML allows systems to learn patterns from data.

 DL leverages deep neural networks for high-complexity tasks like image or speech
recognition.

Use Cases:

 AI: Chatbots, route optimization

 ML: Predictive maintenance in manufacturing

 DL: Voice assistants, autonomous driving

Key Terms:

 Neural networks, supervised/unsupervised learning, gradient descent, backpropagation,

overfitting, hyperparameter tuning

🔹 4. IoT & IIoT

Explanation:

 IoT connects consumer devices for automation and monitoring.

 IIoT applies similar principles in industrial settings with ruggedized sensors and real-time
analytics for operational efficiency.

Use Cases:

 Smart homes with thermostat automation

 Oil rigs using vibration sensors for equipment health

 Cold chain logistics using GPS + humidity trackers

Key Terms:

 Edge computing, MQTT, OPC-UA, digital twins, embedded systems, SCADA, LPWAN,
telemetry
🔹 5. Sensor Fusion

Explanation: Merging data from multiple sensors to improve system accuracy, reduce noise, and
enhance decision-making. It's key in robotics and real-time control systems.

Use Cases:

 Autonomous vehicles combining LiDAR + radar + cameras

 AR/VR headsets aligning motion with visuals

 Drones stabilizing flight using IMU + barometer + GPS

Key Terms:

 Kalman filter, complementary filter, SLAM, IMU, sensor calibration, multi-modal fusion

🔹 6. Cybersecurity

Explanation: The discipline of protecting systems, networks, and data from cyberattacks using
preventive and reactive controls across software, hardware, and user layers.

Use Cases:

 Implementing MFA in enterprise logins

 Using EDR solutions to detect ransomware

 Running phishing simulations for employee training

Key Terms:

 Zero Trust, IAM, SIEM, encryption (AES, RSA), firewalls, penetration testing, threat modeling,
EDR

🔹 7. Blockchain

Explanation: A decentralized, tamper-proof ledger used for transparent and secure record-keeping. It
enables smart contracts and removes intermediaries in many systems.

Use Cases:

 Smart contracts for escrow in real estate

 Blockchain for supply chain traceability (e.g., luxury goods)

 Voting systems with immutability and audit trails

Key Terms:

 Distributed ledger, consensus (PoW, PoS), Merkle tree, tokenization, dApps, smart contracts,
gas fees

🔹 8. Quantum Computing
Explanation: Quantum computing uses qubits and principles like superposition and entanglement to
perform parallel computations, useful in optimization, cryptography, and simulation.

Use Cases:

 Drug discovery via molecule simulation

 Breaking traditional cryptographic algorithms

 Traffic flow optimization in smart cities

Key Terms:

 Qubit, superposition, entanglement, quantum gates, Shor’s algorithm, Qiskit, quantum

supremacy, decoherence

🔹 9. DevOps

Explanation: DevOps combines development and operations to accelerate software delivery through
automation, collaboration, and monitoring. It emphasizes CI/CD pipelines, infrastructure as code,
and observability.

Use Cases:

 Automating deployment of microservices

 Scaling apps using Docker + Kubernetes

 Infrastructure provisioning with Terraform or Ansible

Key Terms:

 CI/CD, GitOps, IaC, blue-green deployment, Jenkins, Docker, Kubernetes, observability,

pipelines

🔹 10. SRE (Site Reliability Engineering)

Explanation: SRE applies software engineering to IT operations, aiming for scalable and reliable
systems. It emphasizes measurable reliability via SLAs, SLOs, and error budgets.

Use Cases:

 Proactively managing latency with custom SLOs

 Automating incident detection and mitigation

 Running chaos experiments to validate recovery mechanisms

Key Terms:

 SLA, SLO, error budgets, observability, toil, runbooks, chaos engineering, blameless
postmortem
🔹 11. Semiconductor Manufacturing Process

Explanation: The multi-step process to fabricate chips on silicon wafers, involving photolithography,
doping, etching, and packaging in ultra-clean environments.

Use Cases:

 5nm chips in mobile SoCs (e.g., Apple’s A-series)

 Automotive microcontrollers for ADAS

 High-performance GPUs and NPUs for AI workloads

Key Terms:

 Photolithography, ion implantation, wafer, EUV lithography, cleanroom, CMP, die shrink,
foundry, fabless

🔚 Closing Statement (Optional for Interview)

"This overview reflects both my hands-on exposure and strategic understanding of foundational and
frontier tech areas. I believe that by communicating through both key terms and relatable use cases,
I can bridge technical depth with stakeholder clarity — whether it’s building scalable AI systems,
securing environments, or optimizing cloud-native deployments."

Let me know if you'd like this formatted into a PDF, presentation deck, or even spoken script for
interview prep.

You said:

difference between agentic ai gen ai prompt eng +ai ml and dl in table format

ChatGPT said:

Sure! Here's a clean and interview-ready comparison table that shows the difference between
Agentic AI, Generative AI, Prompt Engineering, AI, ML, and DL across multiple dimensions like
definition, capability, use cases, and more.

✅ Comparison Table: Agentic AI vs GenAI vs Prompt Engineering vs AI vs ML vs DL

Examples / Use
Concept Definition Key Characteristics Tools / Models
Cases

AI systems that can

Goal-driven, tool-
autonomously plan, AutoGPT, AI
using, multi-step AutoGPT, BabyAGI,
Agentic AI act, and make assistants completing
reasoning, ReAct, LangGraph
decisions in pursuit complex tasks
autonomous agents
of goals.

Generative Subfield of AI that Content creation, ChatGPT, DALL·E, GPT, DALL·E, Claude,
AI focuses on creativity, GitHub Copilot, text-
 Examples / Use
Concept Definition Key Characteristics Tools / Models
Cases

generating new
content (text, code, transformer models to-image generation Stable Diffusion
images, etc.)

The practice of
Zero-shot, few-shot, Prompt tuning for ChatGPT, Claude,
Prompt designing input
instruction tuning, accurate answers or Gemini,
Engineering prompts to guide
token control tone control OpenPromptTools
responses from LLMs.

Umbrella field of
Problem-solving, Virtual assistants, General field
creating machines
AI reasoning, learning, recommendation (includes ML, DL,
that mimic human
decision-making systems GenAI)
intelligence

Subset of AI where
Supervised,
systems learn Spam filters, fraud
ML (Machine unsupervised, Scikit-learn,
patterns from data detection, predictive
Learning) reinforcement XGBoost, TensorFlow
without explicit analytics
learning
programming

Subset of ML using Neural nets, large

Image recognition,
DL (Deep neural networks with data volumes, TensorFlow, PyTorch,
speech recognition,
Learning) many layers to model automatic feature Hugging Face
NLP
complex data learning

🔍 Summary Tips for Interview

 Agentic AI = "AI with autonomy and memory that acts like a human agent."

 Generative AI = "AI that creates — like text, images, code."

 Prompt Engineering = "How we talk to GenAI to get quality output."

 AI = "The big umbrella that covers all kinds of smart systems."

 ML = "AI that learns from data patterns."

 DL = "ML powered by deep neural networks — often for vision and language."

Let me know if you want a visual version, a PDF cheat sheet, or want to add LLMOps, AGI, or other
emerging terms.

🔹 MLOps (Machine Learning Operations)

✅ Definition:
MLOps is the practice of applying DevOps principles to the machine learning lifecycle. It combines
ML model development with continuous integration, deployment, monitoring, and governance to
streamline and scale AI/ML projects in production.

Think of it as DevOps for ML, but with added complexity — because ML models change over time,
need retraining, and are sensitive to data drift.

🔧 Key Concepts in MLOps:

Component Description

Model Training Building models with labeled data using ML algorithms

Model Versioning Tracking different versions of models, datasets, and experiments

Model Deployment Serving trained models via APIs or pipelines

Model Monitoring Tracking performance in production (e.g., accuracy, latency, drift)

Data & Model Drift Handling changes in data distribution or model behavior over time

CI/CD for ML Automating the ML lifecycle: retraining, testing, deployment

Feature Store Managing reusable features across different ML pipelines

Reproducibility Ensuring experiments can be reliably replicated with the same results

🧠 Use Cases:

 Retail: Auto-retraining demand forecasting models when seasonal data shifts

 Healthcare: Monitoring clinical prediction models for data drift (e.g., new patient
demographics)

 Finance: Updating fraud detection systems as fraud tactics evolve

Key Tools in MLOps Stack:

Stage Tools / Frameworks

Experiment Tracking MLflow, Weights & Biases, Neptune

Model Training TensorFlow, PyTorch, Scikit-learn

CI/CD Pipelines GitHub Actions, Jenkins, Kubeflow Pipelines, Airflow

Deployment Seldon, BentoML, KServe, SageMaker

Monitoring Evidently AI, WhyLabs, Prometheus + Grafana

Stage Tools / Frameworks

Feature Store Feast, Tecton

🧩 Comparison: MLOps vs DevOps vs Traditional ML

Traditional ML (without
Topic MLOps DevOps
Ops)

End-to-end ML lifecycle in Software development Experimentation and

Focus
production lifecycle research

Model retraining, testing, Code testing, deployment Manual scripts and

Automation
deployment pipelines notebooks

Drift, model accuracy, data Uptime, CPU usage, error Rarely monitored after
Monitoring
quality rates deployment

Tools MLflow, Kubeflow, Seldon Jenkins, Docker, Kubernetes Jupyter, local training