The AI-Ready Data Engineer

Chapter 3: The One-Month Program

Build real production AI skills in four weeks: advanced RAG techniques, multi-agent frameworks with LangGraph and CrewAI, LLMOps monitoring, and fine-tuning with LoRA and QLoRA.

Week 1: Advanced RAG techniques

Check out NirDiamant's RAG_Techniques repo on GitHub. It's a goldmine. The guy implemented every RAG technique that matters: HyDE, Multi-Query, Reranking, all with working code.

DeepLearning.AI's RAG course is worth the time. They use real datasets and show you how to use Phoenix for monitoring (important for cost management).

For vector databases, actually benchmark them with YOUR data. Pinecone vs Weaviate vs Qdrant: they all have trade-offs. Spend time on chunking strategies. This makes or breaks your RAG system.

RAGAS is the go-to for evaluation. Set it up early, thank me later.

Week 2: Agent frameworks and systems

LangGraph fundamentals

Harrison Chase (LangChain founder) teaches the DeepLearning.AI course on LangGraph. The LangChain Academy has free courses too. LangGraph lets you build agents with actual memory and state, a significant improvement over stateless approaches. LangGraph 1.0 shipped in October 2025 and is now production-ready: durable execution (agents resume after server restarts), first-class streaming, human-in-the-loop pausing, and persistent memory management are all stable.

Multi-agent systems

CrewAI is interesting for multi-agent systems. You create agent teams with roles like "researcher" and "writer" and they actually collaborate. Microsoft's AutoGen is more enterprise-y but worth exploring. Note that AutoGen has split: Microsoft shipped a full architectural rewrite (v0.4, actor model) in January 2025, while the original creators forked it as AG2, which maintains backward compatibility with 0.2. Pick based on whether you need stability or the newer distributed architecture.

For data engineering specifically, Altimate Code takes a different approach: rather than a general-purpose agent framework, it's a domain-specific harness with pre-built tools for dbt, SQL, and warehouse operations. Worth comparing against the general frameworks to see the trade-offs between flexibility and out-of-the-box data awareness.

NirDiamant's GenAI_Agents repo has production examples with proper error handling and state management. Study these before you build anything serious.

As you start building agents against real data estates, you'll hit a wall that has nothing to do with the framework you chose. Agents produce confident but wrong outputs when they lack the organizational context behind your data: why a model was built a certain way, which downstream dashboards depend on it, what compliance constraints apply. This is context debt in action. The frameworks here teach you how to build capable agents; the hard work is making sure those agents have the context they need to act safely.

Week 3: LLMOps and production deployment

Monitoring and observability tools

LangSmith vs Helicone vs LangFuse: try them all. Helicone is easiest to start with (one line integration), LangSmith has the best debugging tools, LangFuse is open source. If evaluation is your primary concern, Braintrust has emerged as a strong dedicated eval platform — worth a look if you want something purpose-built for testing rather than tracing.

MLflow for LLMs — experiment tracking and model registry.

Prompt optimization

DSPy from Stanford automates prompt optimization. It's like hyperparameter tuning but for prompts. Innovative approach to prompt engineering. Learn Prompting has interactive modules that are actually fun.

Cost optimization

This is where you save your company thousands. Implement caching (often 50% cost reduction), use smaller models where possible, batch requests. Neptune.ai has a solid LLMOps guide.

Week 4: Fine-tuning on a budget

LoRA and QLoRA techniques

These techniques let you fine-tune huge models on normal GPUs. The QLoRA paper implementation is the reference.

Unsloth has become the default starting point for efficient fine-tuning: 2x faster training, 70% less VRAM, works with LoRA and QLoRA across Llama, DeepSeek, and Qwen models. Start here before reaching for the heavier tools. Axolotl makes fine-tuning almost point-and-click. LLaMA-Factory has a web UI for those who prefer graphical interfaces.

What you'll have built

Multiple RAG system implementations
Multi-agent systems with proper error handling
A fine-tuned model for your specific use case
Production monitoring and debugging setup

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