Full Stack Generative AI Career Path- Beginners

Learn to design, optimize, and evaluate prompts for effective AI interaction, including system workflows and ethical considerations.

• 1.1 Foundations of Prompt Engineering:
  Understand key concepts, LLM behavior, and optimization techniques.

• 1.2 Intermediate Prompting Techniques:
  Explore dynamic, chain-of-thought, and few-shot prompting through hands-on applications.
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• 1.3 Advanced Prompting Strategies:
  Master prompts for specific use cases, multimodal models, and advanced techniques.

• 1.4 Prompt Engineering in System Design:
  Design workflows, manage context, and create meta-prompts for scalable systems.
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• 1.5 Optimization and Evaluation:
  Optimize performance, balance cost, and evaluate output accuracy using tools like RLHF.

• 1.6 Ethical Considerations:
  Address bias, fairness, and transparency while crafting interpretable prompts.

Learn to develop advanced LLM-powered applications using LangChain’s tools and workflows.

• 2.1 Introduction to LangChain & Chatbot Mechanics:
  Understand LangChain components and build chatbots with memory strategies.

• 2.2 Chains and Agents:
  Design sequential chains, integrate custom tools, and master LangChain Expression Language.
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• 2.3 Retrieval Augmented Generation (RAG):
  Create RAG applications with vector databases and context-rich retrieval prompts.

Explore LangGraph workflows to create intelligent systems with memory and user interaction.

• 3.1 Introduction to LangGraph:
  Build simple workflows with agents, memory, and deployment basics.
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• 3.2 State and Memory:
  ‍Master state management and memory-enabled chatbot development.

• 3.3 UX and Human-in-the-Loop:
  Enhance user experience with dynamic feedback and real-time workflows.
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• 3.4 Building Your Assistant:
  Design complex workflows using parallelization and map-reduce techniques.

• 3.5 Long-Term Memory:
  ‍Integrate schemas and datasets for personalized long-term memory solutions.

Build and optimize RAG systems for knowledge-based AI applications.

• 4.1 Introduction to RAG:
  Understand RAG systems, components, and real-world applications.
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• 4.2 Data Preparation and Indexing:
  Learn chunking strategies and indexing for optimal retrieval.
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• 4.3 Query Optimization:
  Enhance retrieval relevance with query compression and optimization pipelines.
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• 4.4 Retriever Implementation:
  Implement and refine dense, sparse, and hybrid retrieval methods.
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• 4.5 The Generative Component:
  Use transformer models and fine-tuning for effective generative pipelines.

Develop intelligent workflows using LlamaIndex for data management and querying.

• 5.1 Introduction to LlamaIndex and LLM Basics:
  Set up workflows and understand LlamaIndex architecture.
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• 5.2 Core Concepts:
  Explore privacy best practices and address security challenges in LLM applications.
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• 5.3 LlamaIndex Fundamentals:
  Integrate LLMs and compare popular models for performance.
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• 5.4 Practical Implementation:
  Build and optimize data indexes for efficiency.
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• 5.5 Advanced Prompt Engineering:
  Refine query performance and utilize role-based strategies.
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• 5.6 Data Indexing:
  Master embedding models, metadata handling, and vector storage.
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• 5.7 Querying Data:
  Explore natural language querying and external source integration.

Leverage Hugging Face tools to build and deploy NLP and ML applications.

• 6.1 Introduction to Hugging Face:
  Navigate tools like transformers, datasets, and tokenizers.
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• 6.2 Transformers and Pre-Trained Models:
  Use and fine-tune pre-trained transformer models.
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• 6.3 Tokenization and Data Preparation:
  Master tokenization and preprocess datasets for NLP tasks.
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• 6.4 Training and Fine-Tuning:
  Build training pipelines and optimize model performance.
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• 6.5 Deployment and Inference:
  Deploy models using APIs and scalable solutions like Hugging Face Spaces.
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• 6.6 Advanced Topics:
  Explore custom tokenizers, generative AI, and large dataset handling.

Evaluate your understanding and practical application of generative AI concepts, workflows, and tools through real-world scenarios and hands-on exercises.

• Module 1: Assess the ability to design and optimize prompts, develop workflows, and address ethical considerations.
• Module 2: Evaluate chatbot mechanics, chain workflows, and RAG integration via practical projects.
• Module 3: Test skills in building LangGraph workflows, managing state and memory, and creating long-term memory-enabled applications.
• Module 4: Assess proficiency in RAG systems, data preparation, retriever implementation, and generative pipelines.
• Module 5: Measure understanding of LlamaIndex fundamentals, data indexing, querying, and automation by building functional workflows.
• Module 6: Test expertise in Hugging Face tools, including model fine-tuning, tokenization, and deployment.
• Final Assessment: Integrate learnings across modules in a comprehensive evaluation project.

Apply your knowledge to create impactful AI-driven applications tailored to solve real-world challenges in your domain.

• Module 1: Develop a domain-specific AI assistant with advanced prompts and workflows.
• Module 2: Create a LangChain-powered application integrating RAG workflows and memory-enabled chatbots.
• Module 3: Build and deploy a LangGraph application with memory, advanced workflows, and long-term personalization.
• Module 4: Design a RAG system addressing challenges with data retrieval, query optimization, and generative AI pipelines.
• Module 5: Build an intelligent chatbot using LlamaIndex, combining indexing, querying, and automation.
• Module 6: Develop and deploy an NLP application using Hugging Face tools for dataset preparation, model fine-tuning, and scalable deployment.