GenAI Roadmap

Phase 1: Foundations (Weeks 1–2)

Python refresher - Focus on understanding data types and control structures.
- Tip 1: Use online platforms like LeetCode for practice.
- Tip 2: Write small scripts to automate daily tasks.
- Tip 3: Join Python communities for support and resources.
Basic AI/ML concepts - Learn about model evaluation metrics.
- Tip 1: Start with simple datasets like Iris or MNIST.
- Tip 2: Visualize data to understand patterns.
- Tip 3: Use online courses for structured learning.

Phase 2: Meet the LLMs (Weeks 3–4)

Explore major LLMs - Experiment with different prompt styles.
- Tip 1: Compare outputs to understand model biases.
- Tip 2: Use diverse prompts to test model capabilities.
- Tip 3: Document your findings for future reference.
Hands-on with free/open models - Try fine-tuning a small model.
- Tip 1: Use Google Colab for free GPU access.
- Tip 2: Start with pre-trained models to save time.
- Tip 3: Experiment with different hyperparameters.

Phase 3: Build with Frameworks (Weeks 5–6)

LangChain basics - Understand the concept of agents.
- Tip 1: Start with simple workflows.
- Tip 2: Use visual tools to map out processes.
- Tip 3: Collaborate with peers to share insights.
Hugging Face Transformers - Learn about tokenization.
- Tip 1: Use pre-trained models to save time.
- Tip 2: Explore different tokenization techniques.
- Tip 3: Join forums to discuss challenges and solutions.
LangGraph intro - Design a basic agentic workflow.
- Tip 1: Visualize your workflow for better understanding.
- Tip 2: Use feedback loops to refine processes.
- Tip 3: Test workflows with real-world scenarios.

Phase 4: Data & Storage (Weeks 7–8)

Vector databases - Practice indexing and querying.
- Tip 1: Use small datasets to start.
- Tip 2: Experiment with different indexing strategies.
- Tip 3: Monitor performance to optimize queries.
Graph databases - Model relationships effectively.
- Tip 1: Use Neo4j's browser for interactive queries.
- Tip 2: Visualize graphs to identify patterns.
- Tip 3: Practice writing complex Cypher queries.

Phase 5: RAG & Chat over Docs (Weeks 9–10)

Retrieval-Augmented Generation (RAG) - Combine embeddings with LLMs.
- Tip 1: Use vector databases for efficient retrieval.
- Tip 2: Test with various document types.
- Tip 3: Optimize embeddings for better performance.
Context-aware chatbots - Implement memory for better interactions.
- Tip 1: Use session storage for short-term memory.
- Tip 2: Design chatbots to handle diverse queries.
- Tip 3: Continuously update knowledge base.

Phase 6: Agentic Workflows & Memory (Weeks 11–12)

AI agents - Develop agents that can perform tasks autonomously.
- Tip 1: Start with simple tasks and gradually increase complexity.
- Tip 2: Use modular design for flexibility.
- Tip 3: Test agents in controlled environments.
Agent memory - Persist context for long-term memory.
- Tip 1: Use databases like Qdrant for efficient storage.
- Tip 2: Implement memory management strategies.
- Tip 3: Regularly update memory with new data.

Phase 7: Advanced Integrations (Weeks 13–14)

Multi-modal LLM apps - Integrate text and image processing.
- Tip 1: Use APIs for seamless integration.
- Tip 2: Test with diverse data types.
- Tip 3: Optimize for performance and accuracy.
Document→Graph + embeddings - Automate document processing.
- Tip 1: Use pre-built libraries to speed up development.
- Tip 2: Validate data integrity regularly.
- Tip 3: Implement error handling mechanisms.

Phase 8: Security & Guardrails (Weeks 15–16)

Prompt filtering - Ensure safe and appropriate content.
- Tip 1: Implement regex filters for quick checks.
- Tip 2: Regularly update filtering criteria.
- Tip 3: Test filters with diverse inputs.
Bias control - Test and mitigate biases in outputs.
- Tip 1: Use diverse datasets for training.
- Tip 2: Implement bias detection tools.
- Tip 3: Continuously monitor outputs for bias.

Phase 9: Orchestration & Tooling (Weeks 17–18)

LangGraph orchestration - Manage complex workflows.
- Tip 1: Use visual tools for better management.
- Tip 2: Implement checkpoints for error handling.
- Tip 3: Regularly review and optimize workflows.
Human-in-the-Loop - Integrate human feedback.
- Tip 1: Use feedback loops to improve accuracy.
- Tip 2: Design interfaces for easy feedback collection.
- Tip 3: Analyze feedback to identify improvement areas.
Tool binding & calling - Connect to external tools.
- Tip 1: Use APIs for easy integration.
- Tip 2: Test connections with various tools.
- Tip 3: Ensure security in data transmission.

Phase 10: Polishing & Fine-Tuning (Weeks 19–20)

LLM as judge - Evaluate model outputs.
- Tip 1: Use metrics like BLEU or ROUGE for evaluation.
- Tip 2: Compare outputs with human judgments.
- Tip 3: Regularly update evaluation criteria.
Fine-tune a model - Use domain-specific data.
- Tip 1: Start with small datasets to avoid overfitting.
- Tip 2: Experiment with different learning rates.
- Tip 3: Monitor training progress closely.
Deploy & monitor - Ensure scalability and reliability.
- Tip 1: Use Docker for containerization.
- Tip 2: Implement monitoring tools for real-time insights.
- Tip 3: Regularly update and patch systems.

Final Projects

✅ AI-Powered Legal Document Assistant
- Point 1: Automate the extraction of key legal terms and clauses.
- Point 2: Implement natural language processing to understand context.
- Point 3: Ensure compliance with legal standards and regulations.
- Tip 1: Use pre-trained NLP models for legal text analysis.
- Tip 2: Collaborate with legal experts for domain knowledge.
- Tip 3: Test with a variety of legal documents for robustness.
- Before: Familiarize with legal terminology and document structures.
✅ AI-Powered Chart Builder with Postgres
- Point 1: Integrate AI to suggest optimal chart types based on data.
- Point 2: Use Postgres for efficient data storage and retrieval.
- Point 3: Implement interactive features for user engagement.
- Tip 1: Use libraries like D3.js for dynamic chart rendering.
- Tip 2: Optimize database queries for performance.
- Tip 3: Ensure charts are accessible and user-friendly.
- Before: Understand data visualization principles and SQL basics.
✅ AI-Powered Resume Roasting
- Point 1: Analyze resumes to provide constructive feedback.
- Point 2: Highlight strengths and areas for improvement.
- Point 3: Use AI to match resumes with job descriptions.
- Tip 1: Train models on a diverse set of resumes and job postings.
- Tip 2: Incorporate feedback from HR professionals.
- Tip 3: Ensure privacy and security of user data.
- Before: Study resume writing best practices and industry standards.
✅ AI-Powered Candidate Search
- Point 1: Develop algorithms to identify top candidates from databases.
- Point 2: Use AI to rank candidates based on skills and experience.
- Point 3: Implement a user-friendly interface for recruiters.
- Tip 1: Use machine learning to improve search accuracy over time.
- Tip 2: Regularly update the candidate database for relevance.
- Tip 3: Provide filters and sorting options for customized searches.
- Before: Learn about recruitment processes and candidate evaluation.
✅ AI-Powered Website Bot (Chat with Website)
- Point 1: Enable real-time interaction with website visitors.
- Point 2: Use AI to understand and respond to user queries.
- Point 3: Integrate with existing website infrastructure seamlessly.
- Tip 1: Use conversational AI frameworks for natural interactions.
- Tip 2: Continuously train the bot with new data for improvement.
- Tip 3: Monitor bot performance and user satisfaction regularly.
- Before: Familiarize with web development and chatbot technologies.