Become an AI Engineerin 8 Weeks

Theory

• Core LLM terminology: tokens, context window, parameters, attention mechanism

• How LLMs are trained: pre-training, post-training, and inference at runtime

• Embeddings & semantic similarity: turning text into vectors

• Prompting basics: system, user, few-shot

• Structured outputs and JSON formatting 

Coding

• Make your first LLM API calls, and compute cosine similarity from scratch

Theory

• Why RAG exists: knowledge cutoff and hallucination problem

• The 5-stage RAG pipeline: ingest → chunk → embed → index → retrieve

• Chunking strategies and when to use each

• Embedding models: choosing the right one for your use case

• Vector databases & indexing algorithms (HNSW, IVF, PQ)

Coding

• Build an end-to-end RAG pipeline with LangChain + ChromaDB.

Theory

• Why naive RAG hits accuracy ceilings

• Query rewriting techniques: expansion, HyDE, multi-query

• Cross-encoder reranking: LLM-quality scoring after initial retrieval

• Metadata filtering and hybrid search (dense + sparse)

• Evals specific to RAG: faithfulness, answer relevance.

Coding

• Apply advanced retrieval techniques to your Week 2 pipeline and benchmark accuracy improvements with an eval suite

Theory

• Common pitfalls in production RAG systems

• GraphRAG: knowledge graphs as retrieval backends

• KAG (Knowledge-Augmented Generation): structured KB + LLM

• Agentic RAG: LLM decides what to retrieve, when, and how

• Choosing the right architecture: a decision framework for selection

Coding

Build a GraphRAG system on a real use-case using Neo4j, and compare it against vanilla RAG

Theory

• What is an agent? LLM + tools + loop

• LLM vs. Agent: how to distinguish pipeline vs single api call

• Tool / function calling: how the LLM triggers Python functions

• The ReAct pattern: Reasoning → Acting → Observing

• Pydantic AI: type-safe agents with validated structured outputs

Coding

• Build a single agent with tools, structured outputs, and basic guardrails.

Theory

• Why multi-agent? Parallelism, specialisation, separation of concerns

• Agentic design patterns: Orchestrator-Worker, Routing

• Problems unique to multi-agent: orchestration, information isolation, planning

• Memory systems for agents: short-term and long-term

• Cost and latency considerations in multi-agent systems 

Coding

• Build a research + writer + critic multi-agent pipeline in LangGraph with routing.

Theory

• Context engineering vs. prompt engineering

• What goes in the context: instructions, tools, history, retrieved docs

• Quantitative Evals (accuracy, F1, exact match, tool-call accuracy)

• Qualitative evals with LLM-as-a-Judge: rubric design, single-answer grading.

Coding

• Run a full eval suite on your Week 6 multi-agent pipeline with both quantitative and LLM-as-a-Judge evaluations

Theory

• AI engineering best practices: from prototype to production

• Engineering decision framework: choosing RAG type, agent pattern, and stack

• How to scope a capstone: MVP definition, what to cut, what to keep

• Architecture reviews and 1:1 feedback on each project plan

• Presenting AI systems in interviews: structure, demo flow, eval results

Coding

Live capstone demos with peer Q&A, eval walkthroughs, and structured feedback