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Taki089.Dang
Taki089.Dang

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Roadmap for Gen AI dev in 2025

To build a Generative AI (GenAI) application using LangChain, RAG (Retrieval-Augmented Generation), and OpenAI, you'll need to master several concepts and tools. Below is a step-by-step roadmap, organized into foundational, intermediate, and advanced phases.

Based on my experience, I don't think it's ideal, but all the keywords below will help you get an overview when planning to build a Gen AI app. :)))


Phase 1: Foundations

Step 1: Understand AI/ML Basics

  • Learn the fundamentals of AI/ML:
    • Supervised, unsupervised, and reinforcement learning.
    • Natural Language Processing (NLP) basics.
  • Resources:

Step 2: Programming Proficiency

  • Languages: Python and/or TypeScript (preferred for LangChain with Node.js).
  • Key skills:
    • Handling JSON, APIs, and data formats.
    • Writing modular, reusable code.
  • Resources:
    • FreeCodeCamp, Codecademy, or YouTube tutorials.

Step 3: Cloud Basics

  • Learn cloud platforms: AWS, Azure, or GCP.
  • Focus on:
    • Setting up VMs, databases, and APIs.
    • Storing files (e.g., AWS S3, MongoDB).
  • Resources:

Phase 2: Intermediate

Step 4: LangChain Fundamentals

  • Study LangChain documentation and concepts:
    • Chains, Agents, Prompts.
    • Memory management.
    • Integrations with vector stores (e.g., Pinecone, MongoDB).
  • Resources:

Step 5: Learn about RAG (Retrieval-Augmented Generation)

  • Understand the RAG pipeline:
    • Chunking documents into embeddings.
    • Storing and retrieving relevant data from vector stores.
    • Using retrieved context to augment prompts for generation.
  • Practice tools:
    • LangChain for document splitting and embeddings.
    • Vector databases like Pinecone, Weaviate, or MongoDB Atlas with vector search.
  • Resources:
    • Tutorials: LangChain RAG examples.
    • Blog posts on OpenAI RAG workflows.

Step 6: OpenAI APIs

  • Learn how to use OpenAI APIs:
    • Fine-tune GPT models.
    • Use Embedding Models (e.g., text-embedding-ada-002) to generate vector representations.
    • Best practices for prompt engineering.
  • Resources:

Step 7: Work with Vector Stores

  • Understand how vector stores operate:
    • Similarity search and storage.
    • Choosing the right store (e.g., Pinecone, Weaviate, MongoDB).
  • Learn integrations with LangChain.
  • Resources:

Step 8: Integrate NLP Tools

  • Preprocessing:
    • Tokenization, stopword removal, stemming/lemmatization.
  • Tools:
    • Spacy, Hugging Face, or Natural.js (for TypeScript).
  • Resources:
    • NLP in Action by Hobson Lane.

Phase 3: Advanced

Step 9: Build a RAG Workflow

  • Set up the RAG pipeline end-to-end:
    1. Ingest documents.
    2. Chunk documents (LangChain or custom scripts).
    3. Generate embeddings (OpenAI or Hugging Face models).
    4. Store embeddings in a vector database.
    5. Retrieve context and feed it to a generative model.
    6. Output meaningful results.
  • Resources:
    • LangChain RAG demo code.

Step 10: Hugging Face and Transformers

  • Learn to use Hugging Face Transformers for custom model workflows:
    • Create embeddings locally using models like BERT.
    • Fine-tune existing Hugging Face models for specific tasks.
  • Resources:
    • Hugging Face courses and docs.
    • Tutorials on fine-tuning BERT or GPT models.

Step 11: Backend Development with LangChain

  • Use LangChain with NestJS or other backend frameworks.
    • Set up APIs to expose LangChain pipelines.
    • Secure endpoints with authentication (e.g., JWT, OAuth).
  • Resources:
    • NestJS tutorials.
    • LangChain backend integration examples.

Step 12: Optimize Prompt Engineering

  • Experiment with various prompt styles.
  • Fine-tune models or use OpenAI Playground for better results.
  • Resources:
    • OpenAI Prompt Engineering Guide.
    • LangChain prompt templates.

Step 13: Deploy to Production

  • Set up your GenAI app in production:
    • Use Docker and Kubernetes for containerization and scaling.
    • Optimize cost: Use GPU instances only when necessary.
    • Monitor API usage and rate limits.
  • Resources:
    • Tutorials on deploying AI apps.
    • Tools: Docker, AWS/GCP deployment guides.

Step 14: Learn UX/UI for AI Apps

  • Build a user-friendly frontend for interaction:
    • Use React or Angular for frontend development.
    • Integrate LangChain APIs.
    • Resources:
    • Frontend tutorials (React, Angular).

Step 15: Continuous Learning and Scaling

  • Learn:
    • Model fine-tuning.
    • Dataset preparation for improved accuracy.
    • Advanced vector database techniques.
  • Explore:
    • Distributed systems for scaling AI apps.
    • Multi-modal AI (e.g., combining text and images).

Suggested Timeline

Week Topics
1-2 AI/ML basics, programming, cloud setup.
3-4 LangChain fundamentals, OpenAI API use.
5-6 RAG, vector stores, NLP preprocessing.
7-8 Build and test RAG workflows.
9-10 Hugging Face, backend integration.
11-12 Deployment, UI/UX for GenAI apps.

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