Gen AI Sessions Portal 01
Empowering Innovation with Generative Artificial Intelligence
We are committed to helping organizations accelerate digital transformation, automate complex workflows, enhance decision-making, and unlock new opportunities through responsible and scalable AI technologies.
Key Objectives of Our Gen AI Portal
- To democratize AI innovation by providing easy-to-use tools for creating AI-powered applications, assistants, and intelligent workflows.
- To accelerate business productivity through AI-driven automation, content generation, data analysis, and process optimization.
- To enable intelligent decision-making using advanced analytics, predictive modeling, and real-time AI insights.
- To provide secure and scalable AI infrastructure that supports enterprise-grade deployments across industries.
- To empower developers and researchers with access to powerful AI models, APIs, datasets, and experimentation environments.
- To bridge the gap between human creativity and machine intelligence by enabling collaborative AI-assisted innovation.
- To support multi-domain AI solutions including chatbots, virtual assistants, document intelligence, code generation, image generation, recommendation systems, and knowledge management.
- To drive digital transformation across industries such as healthcare, education, finance, manufacturing, biotechnology, renewable energy, embedded systems, and enterprise operations.
- To foster responsible AI adoption through ethical AI practices, transparency, security, and compliance standards.
- To transform ideas into intelligent products by providing end-to-end AI development, deployment, and optimization capabilities.
🧭 TASK 1: Introduction to Generative AI and LLMs
🎯 Objective:
The primary objective of this session is to establish a solid foundational understanding of Generative Artificial Intelligence and Large Language Models (LLMs). Students will bridge the gap between traditional predictive machine learning and generative architectures. By the end of this task, you will comprehend the transition from basic statistical models to foundational transformer models, enabling you to identify optimal business use cases and avoid architectural misconceptions.
🎬 Session:
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📝 Task:
Write notes on:
Introduction to Generative AI, Discriminative AI, and Deep Learning Foundations
Evolution of Language Models: From N-grams to Transformers
Core Concepts of Tokens, Context Windows, and Parameters
How LLMs Predict text: Probability Distribution and Next-Token Prediction
Overview of Open-Source vs. Proprietary AI Models
Computational Requirements and the Scale of Modern Foundational Models
🔬 Practice / Research:
Research how foundational models use raw text corpora for unsupervised pre-training. Write a short summary explaining why tokenization choice (e.g., Byte-Pair Encoding) affects how a model interprets non-English languages or source code. List 3 examples of everyday commercial products completely powered by generative text backends.
📄 Practice Activity:
Write a short paragraph explaining: “How understanding the limitations of an LLM’s context window helps an AI engineer avoid application errors and optimization inefficiencies.”
📊 Internship Task Completion Status Form:
After completing this task, interns must fill out the Internship Task Completion Status Form and upload their notes/practice work.
🔗 Form Link: https://forms.gle/kArakCDLG6aeCn218
🧭 TASK 2: Exploring and Comparing Different LLMs
🎯 Objective:
The primary objective of this session is to evaluate and analyze the current landscape of competitive Large Language Models. Students will learn to distinguish models based on parameter size, benchmarking datasets, license terms, and operational trade-offs. By the end of this task, you will be able to select the most cost-effective and performant model for specific industry constraints.
🎬 Session:
👉Click here to Watch your uploaded session
📝 Task:
Write notes on:
Key Industry Players: OpenAI, Google, Anthropic, Meta, and Mistral AI
Benchmarking Metrics: Understanding MMLU, HumanEval, and GSM8K scores
Proprietary APIs vs. Local Open-Weights Deployments
Cost-per-Token Structures and Latency Trade-offs
Model Quantization: How 4-bit and 8-bit weights affect local performance
Privacy, Data Governance, and Sovereignty in Enterprise Model Selection
🔬 Practice / Research:
Research the difference between Meta’s Llama architecture and OpenAI’s GPT models regarding fine-tuning flexibility. Write a short summary explaining why a developer might choose a smaller, fine-tuned open-source model over a massive proprietary commercial API. List 5 open-source model repositories currently trending on Hugging Face.
📄 Practice Activity:
Write a short paragraph explaining: “How evaluating model size versus inference speed allows a systems architect to optimize cost and UX in production environments.”
📊 Internship Task Completion Status Form:
After completing this task, interns must fill out the Internship Task Completion Status Form and upload their notes/practice work.
🔗 Form Link: https://forms.gle/kArakCDLG6aeCn218
🧭 TASK 3: Using Generative AI Responsibly
🎯 Objective:
The primary objective of this session is to instill rigorous standards of ethical AI deployment, safety alignments, and risk mitigation. Students will confront system-level vulnerabilities like data leakage, hallucinations, and weaponized prompt injections. By the end of this task, you will know how to design safety guardrails and implement compliance measures within corporate settings.
🎬 Session:
👉 Click here to Watch your uploaded session
📝 Task:
Write notes on:
Understanding Hallucinations and Grounding Techniques in LLMs
RLHF (Reinforcement Learning from Human Feedback) and Safety Alignment
Data Privacy Regulations: GDPR, CCPA, and Enterprise Data Leaks
Prompt Injection Attacks and Jailbreaking Risks
Copyright, Intellectual Property, and Training Data Fair Use
Bias Mitigation, Toxicity Filtering, and Content Moderation Layers
🔬 Practice / Research:
Research a real-world case study where a brand’s public-facing AI system failed due to inadequate guardrails or prompt injection. Write a short summary explaining the mechanics of how the exploit or hallucination occurred. List 3 open-source libraries or frameworks used explicitly for LLM guardrails and safety verification.
📄 Practice Activity:
Write a short paragraph explaining: “How setting up a multi-layered content moderation API helps a company protect its brand identity from toxic or malicious user inputs.”
📊 Internship Task Completion Status Form:
After completing this task, interns must fill out the Internship Task Completion Status Form and upload their notes/practice work.
🔗 Form Link: https://forms.gle/kArakCDLG6aeCn218
🧭 TASK 4: Understanding Prompt Engineering Fundamentals
🎯 Objective:
The primary objective of this session is to introduce structured linguistic programming for text models. Students will transition from casual chatting to precise engineering by mastering structural components like systemic instructions, boundaries, and expected output shapes. By the end of this task, you will be able to maximize model output predictability without writing system code.
🎬 Session:
👉Click here to Watch your uploaded session
📝 Task:
Write notes on:
The Core Architecture of a Prompt: Role, Context, Task, Constraints, and Examples
Difference between System Prompts, User Prompts, and Assistant Responses
Delimiters: Using XML Tags, Markdown, and JSON to Isolate Inputs
Controlling Output Format: Strict Markdown, Bullets, and Structured Prose
Managing Temperature, Top-P, and Frequency Penalties for Variability
Handling Edge Cases and Graceful Failures via Prompt Instructions
🔬 Practice / Research:
Research the structural anatomy of standard system prompts used by major chat interfaces. Write a short summary explaining why explicit delimiters (like ### or <input>) prevent the model from confusing instruction instructions with user data. List 5 system-level directives commonly found in production-grade prompt templates.
📄 Practice Activity:
Write a short paragraph explaining: “How manipulating temperature settings between 0.0 and 1.0 shifts an LLM from a deterministic logic engine to an imaginative creative generator.”
🔗 Form Link: https://forms.gle/kArakCDLG6aeCn218
🧭 TASK 5: Creating Advanced Prompts
🎯 Objective:
The primary objective of this session is to look past simple text instructions into algorithmic prompting patterns. Students will learn logic patterns like Chain-of-Thought, Meta-Prompting, and Few-Shot demonstrations to handle complex logic, arithmetic, and strict structuring requirements. By the end of this task, you will be capable of forcing models through explicit step-by-step reasoning pipelines.
🎬 Session:
👉 Click here to Watch your uploaded session
📝 Task:
Write notes on:
Zero-Shot vs. Few-Shot Prompting Mechanics
Chain-of-Thought (CoT) Prompting: Forcing Step-by-Step Logic
Self-Consistency and Tree-of-Thoughts (ToT) Frameworks
Least-to-Most Prompting for Complex Decomposition Tasks
Program-Aided Language Models (PAL) and Externalized Logic
Dynamic Prompt Generation and Meta-Prompting Systems
🔬 Practice / Research:
Research how Chain-of-Thought prompting directly scales down mathematical error rates in autoregressive LLMs. Write a short summary explaining why providing synthetic context examples (Few-Shot) changes model accuracy compared to basic command inputs. List 3 distinct reasoning patterns used to debug edge cases in long-context tasks.
📄 Practice Activity:
Write a short paragraph explaining: “How forcing an LLM to state its intermediate logic steps prior to outputting a final response prevents semantic drift and analytical calculation errors.”
📊 Internship Task Completion Status Form:
After completing this task, interns must fill out the Internship Task Completion Status Form and upload their notes/practice work.
🔗 Form Link: https://forms.gle/kArakCDLG6aeCn218
🧭 TASK 6: Building Text Generation Applications
🎯 Objective:
The primary objective of this session is to migrate prompt designs into production software. Students will use modern software environments and official software development kits (SDKs) to build backends capable of calling inference endpoints, parsing data schemas automatically, and scaling batch operations. By the end of this task, you will be comfortable writing programmatic abstractions around language endpoints.
🎬 Session:
👉 Click here to Watch your uploaded session
📝 Task:
Write notes on:
Setting Up API Environments, Authentication, and Secret Management
Using Official SDKs (OpenAI, Google GenAI, Anthropic) in Python/Node.js
Structured Outputs: Guaranteeing Strict JSON Formats via Code
Asynchronous API Calls and Handling Rate Limits (TPM/RPM)
Token Counting, Cost Estimation, and Usage Optimization Modules
Handling Network Failures, Timeouts, and Backoff Retries
🔬 Practice / Research:
Research the concept of function calling or tool use within developer SDKs. Write a short summary explaining why natively forcing a JSON output structure is essential when building pipelines that feed into databases or downstream web apps. List 5 standard Python packages used when developing production AI wrappers.
📄 Practice Activity:
Write a short paragraph explaining: “How building an exponential backoff retry loop protects a production text generation script from crashing during global API outages or rate limit hits.”
📊 Internship Task Completion Status Form:
After completing this task, interns must fill out the Internship Task Completion Status Form and upload their notes/practice work.
🔗 Form Link: https://forms.gle/kArakCDLG6aeCn218
🧭 TASK 7: Building Chat Applications
🎯 Objective:
The primary objective of this session is to master stateful development by implementing context memories, conversational streaming, and interface wrappers. Students will build conversational state tracking to simulate continuous human-machine interactions. By the end of this task, you will know how to architecture full-stack conversational interfaces, window memories, and deployment engines.
🎬 Session:
👉Click here to Watch your uploaded session
📝 Task:
Write notes on:
Stateful vs. Stateless Implementations: Managing Conversational Context
Memory Strategies: Buffer Memory, Summary Memory, and Sliding Window Memory
Streaming Responses (Server-Sent Events) for Real-Time UI updates
System Message Injection for Conversational Tone Guarding
Building Interfaces using Streamlit, Gradio, or Modern Web Frameworks
Scaling Chat Storage: Relational Databases and Document Stores for Chat History
🔬 Practice / Research:
Research why long chat conversations degrade model response times over extended periods. Write a short summary explaining how a sliding window memory approach optimizes both prompt token costs and inference latency in a consumer chat app. List 3 production UI frameworks favored by data scientists for fast deployment of conversational agents.
📄 Practice Activity:
Write a short paragraph explaining: “How implementing chunked token streaming enhances user-perceived performance (TTFT) compared to waiting for a full text response payload.”
📊 Internship Task Completion Status Form:
After completing this task, interns must fill out the Internship Task Completion Status Form and upload their notes/practice work.
🔗 Form Link: https://forms.gle/kArakCDLG6aeCn218