🧠 AI Learning Roadmap for Backend Engineers

📅 Week-by-Week Breakdown

1. Week 1–2: Core Concepts & Mindset
   • Understand the fundamentals of Generative AI and Large Language Models (LLMs).
   • Learn about tokens, embeddings, inference, prompting, and retrieval.
2. Week 3: Prompt Engineering & APIs
   • Explore prompt templates, few-shot prompting, and system messages.
   • Utilize APIs from OpenAI, Anthropic, or Cohere.
3. Week 4–5: Embeddings & Vector Databases
   • Convert text to embeddings.
   • Store and query with FAISS, Pinecone, Weaviate, or Chroma.
4. Week 6–7: Retrieval-Augmented Generation (RAG)
   • Combine vector search with LLMs to answer domain-specific queries.
   • Use LangChain, LlamaIndex, or custom RAG pipelines.
5. Week 8: LangChain / LlamaIndex
   • Orchestrate AI components using LangChain.
   • Index and query your own data with LlamaIndex.
6. Week 9: AI Agents
   • Implement agents that can perform multistep tasks with tools.
   • Use ReAct, AutoGPT, or LangGraph.
7. Week 10: Whisper / Audio → Text (Optional)
   • Transcribe voice/audio for search, RAG, summarization.
   • Use OpenAI Whisper, Deepgram, or AssemblyAI.
8. Ongoing: Backend Integration Patterns
   • Build robust systems with API rate limiting, retries, streaming responses.
   • Secure and cache AI responses; implement observability.

🛠️ Tools & Resources

• APIs: OpenAI, Anthropic, Cohere
• Vector Databases: FAISS, Pinecone, Weaviate, Chroma
• Frameworks: LangChain, LlamaIndex
• Transcription: OpenAI Whisper, Deepgram, AssemblyAI
• Project Ideas:
  • AI-powered document Q&A (RAG over PDF)
  • Voice note summarizer using Whisper + OpenAI
  • Slack/WhatsApp bot using LangChain agents
  • Internal tool using AI to query SQL schemas
  • AI search across Git repos or logs

📚 Additional Resources

• OpenAI Cookbook