Start here instead....

Also, scrolling YouTube playlists or jumping into random courses doesn’t work.

You need a Ai learning roadmap with layers of learning that compound.

𝗛𝗲𝗿𝗲’𝘀 𝗵𝗼𝘄 𝘁𝗼 𝗹𝗲𝗮𝗿𝗻 𝗚𝗲𝗻𝗔𝗜 𝘁𝗵𝗲 𝗿𝗶𝗴𝗵𝘁 𝘄𝗮𝘆:

𝟭. 𝗦𝘁𝗮𝗿𝘁 𝘄𝗶𝘁𝗵 𝘁𝗵𝗲 𝗕𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝗕𝗹𝗼𝗰𝗸𝘀
• Python (requests, APIs, JSON, environments)
• Git + Docker + Linux basics
• Databases (Postgres, SQLite)

𝟮. 𝗟𝗲𝗮𝗿𝗻 𝗛𝗼𝘄 𝗠𝗼𝗱𝗲𝗹𝘀 𝗧𝗵𝗶𝗻𝗸
• Vectors & embeddings
• Probability & tokenization
• Transformers at a high level

𝟯. 𝗣𝗹𝗮𝘆 𝘄𝗶𝘁𝗵 𝗠𝗼𝗱𝗲𝗹𝘀 𝗘𝗮𝗿𝗹𝘆 (𝗯𝘂𝘁 𝘀𝗺𝗮𝗹𝗹 𝘀𝗰𝗮𝗹𝗲)
• Hugging Face inference APIs
• OpenAI / Anthropic playgrounds
• Local models with Ollama

𝟰. 𝗠𝗮𝘀𝘁𝗲𝗿 𝘁𝗵𝗲 𝗥𝗔𝗚 𝗪𝗼𝗿𝗸𝗳𝗹𝗼𝘄
• Ingest → chunk → embed → store → retrieve → re-rank → generate
• Build this manually first (no frameworks)
• Add logging, retries, caching

𝟱. 𝗚𝗲𝘁 𝗦𝗲𝗿𝗶𝗼𝘂𝘀 𝗔𝗯𝗼𝘂𝘁 𝗘𝘃𝗮𝗹𝘂𝗮𝘁𝗶𝗼𝗻
• Compare outputs with ground truth
• Track accuracy, latency, and cost
• Learn prompt evaluation patterns

𝟲. 𝗘𝘅𝗽𝗹𝗼𝗿𝗲 𝗦𝗮𝗳𝗲𝘁𝘆 & 𝗚𝘂𝗮𝗿𝗱𝗿𝗮𝗶𝗹𝘀
• Handle hallucinations & toxicity
• Add redaction for PII
• Experiment with content filters

𝟳. 𝗕𝘂𝗶𝗹𝗱 𝗠𝗶𝗻𝗶-𝗣𝗿𝗼𝗷𝗲𝗰𝘁𝘀
• Document Q&A bot
• Structured extraction (tables/JSON)
• Summarizer with benchmarks

𝟴. 𝗠𝗼𝘃𝗲 𝗧𝗼𝘄𝗮𝗿𝗱 𝗥𝗲𝗹𝗶𝗮𝗯𝗶𝗹𝗶𝘁𝘆 & 𝗠𝗟𝗢𝗽𝘀
• CI/CD for prompts/configs
• Tracing and observability
• Cost dashboards

𝟵. 𝗢𝗻𝗹𝘆 𝗧𝗵𝗲𝗻: 𝗟𝗲𝗮𝗿𝗻 𝗔𝗴𝗲𝗻𝘁𝘀
• Start with one-tool agents
• Add memory/planning when metrics prove value

𝟭𝟬. 𝗙𝗶𝗻𝗮𝗹𝗹𝘆 → 𝗙𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸𝘀
• Use LangGraph, ADK, CrewAI or LlamaIndex as orchestration layers
• Keep your core logic framework-agnostic

👉 The order matters.
👉 Learn why before how.
👉 Projects > tutorials.

That’s how you go from “copy-pasting prompts” → “engineering production-ready GenAI systems.” Show ❤️ if you find this post valuable.

Learn n8n with me:
https://whatsapp.com/channel/0029VbAeZ2SFXUuWxNVqJj22

AI Engineering has levels to it:

– Level 1: Using AI
Start by mastering the fundamentals:
-- Prompt engineering (zero-shot, few-shot, chain-of-thought)
-- Calling APIs (OpenAI, Anthropic, Cohere, Hugging Face)
-- Understanding tokens, context windows, and parameters (temperature, top-p)

With just these basics, you can already solve real problems.

– Level 2: Integrating AI
Move from using AI to building with it:
-- Retrieval Augmented Generation (RAG) with vector databases (Pinecone, FAISS, Weaviate, Milvus)
-- Embeddings and similarity search (cosine, Euclidean, dot product)
-- Caching and batching for cost and latency improvements
-- Agents and tool use (safe function calling, API orchestration)

This is the foundation of most modern AI products.

– Level 3: Engineering AI Systems
Level up from prototypes to production-ready systems:
-- Fine-tuning vs instruction-tuning vs RLHF (know when each applies)
-- Guardrails for safety and compliance (filters, validators, adversarial testing)
-- Multi-model architectures (LLMs + smaller specialized models)
-- Evaluation frameworks (BLEU, ROUGE, perplexity, win-rates, human evals)

Here’s where you shift from “it works” to “it works reliably.”

– Level 4: Optimizing AI at Scale
Finally, learn how to run AI systems efficiently and responsibly:
-- Distributed inference (vLLM, Ray Serve, Hugging Face TGI)
-- Managing context length and memory (chunking, summarization, attention strategies)
-- Balancing cost vs performance (open-source vs proprietary tradeoffs)
-- Privacy, compliance, and governance (PII redaction, SOC2, HIPAA, GDPR)

At this stage, you’re not just building AI—you’re designing systems that scale in the real world.