The GenAI Tools Map: What to Learn, What to Skip in 2024

Feeling overwhelmed by the flood of new AI tools?

One week it's LangChain, the next it's CrewAI. You're afraid of spending months learning a framework only for it to become obsolete. This isn't another vague blog post. This is your opinionated, decisive map through the chaos. We tell you exactly what tools are used in production, what's just Twitter hype, and what you should focus on.

Inside this 61-page guide:

A complete breakdown of the GenAI ecosystem — decision trees for choosing between OpenAI, Anthropic, Google, and open-source LLMs. The precise scenarios for LangChain vs. LlamaIndex vs. raw API calls. Deep dives into vector databases, MLOps platforms, and AI agent frameworks.

What you'll get:

• Decide your core LLM provider with a detailed decision tree comparing OpenAI, Anthropic, Google, and Llama 3
• Choose the right LLM framework — production use cases for LangChain, LlamaIndex, and the Vercel AI SDK
• Select your vector database: ChromaDB for prototyping or Pinecone/Weaviate for production-scale RAG
• Master the MLOps lifecycle — evaluation frameworks to model serving (SageMaker vs. Vertex AI)
• Build your role-specific starter stack — curated toolchains for 5 different career paths
• Determine exactly when to fine-tune vs. rely on advanced prompt engineering
• Compare AI-native code editors (Cursor vs. VS Code + GitHub Copilot)
• Identify which AI certifications actually matter to hiring managers

Who this is for:

Developers, engineers, and technical PMs who need to make smart, strategic decisions about which technologies to invest their time in.

From the creator of AI Career Roadmap, trusted by 113K on YouTube and engineers at Fortune 500 companies.

Includes lifetime quarterly updates. Save dozens of hours of research for less than the cost of a team lunch.