By the TechStudio editorial team · Updated September 25, 2026 · Editorial policy
7-Day AI Engineering Roadmap
Welcome to your 7-Day AI Engineering Roadmap. This intensive, hands-on path takes you from absolute beginner to building, containerizing, and deploying a real-world AI application powered by Large Language Models, Retrieval-Augmented Generation, and AI Agents.
Before you start
Recommended setup
Core tools
- Python 3.10+ and a virtual environment
- VS Code or another Python-friendly editor
- Git and a GitHub account
- Docker Desktop for Day 6–7
- Terminal access and basic command-line comfort
AI provider
- Use OpenAI or Google Gemini for the first API exercises.
- Keep API keys in environment variables, never in Git.
- Set usage limits and monitor API consumption.
- You can swap providers later because the concepts are portable.
Important
The roadmap is intentionally compressed. You are not expected to become a production AI engineer in seven days. The goal is to finish one coherent end-to-end system while learning the major building blocks well enough to continue independently.
Overview
The 7-Day Journey
LLM Fundamentals + API
Understand tokens, context, model calls, streaming and usage.
Prompting + Structured Outputs
Build reliable prompts and validate machine-readable responses.
Embeddings + Vector DB
Turn text into vectors and implement semantic retrieval.
RAG Systems
Build a complete retrieval-to-generation pipeline.
AI Agents
Implement tool use and a ReAct-style decision loop.
FastAPI + Docker
Expose your AI workflow as a containerized API.
Capstone Assistant
Combine upload, RAG, tools, agents, API and Docker.
One portfolio-ready system
A GitHub repository you can explain, demo and keep improving.
AI & LLM Fundamentals + API Integration
Understand how modern LLM applications work, then make your first programmatic call. Focus on the practical mental model rather than trying to learn every transformer detail in one day.
Streaming LLM CLI
Learn
- Tokens and tokenization
- Context windows and input/output tokens
- Transformer and attention intuition
- Temperature and sampling at a high level
- Streaming responses
- API authentication and environment variables
- Basic latency, usage and cost awareness
Practical task
Write a Python script using the official OpenAI or Google GenAI SDK. Send a prompt, stream the response token-by-token, print the final answer, and record whatever usage information the provider exposes.
python -m venv .venv
# activate the environment
pip install openai
# or: pip install -U google-genaiAdvanced Prompt Engineering & Structured Outputs
Move beyond conversational prompting. Learn how system instructions, examples, constraints and schemas make an LLM application easier to integrate with normal software.
Support-ticket classifier
Learn
- System vs. user instructions
- Few-shot examples
- Task decomposition
- Output constraints
- Structured JSON and schemas
- Validation and retry strategies
- Prompt injection awareness
Practical task
Build a Python program that accepts a messy support ticket and returns a validated object containing priority, sentiment, category, short summary and recommended next action. Use Pydantic to validate the response and handle invalid output.
class Ticket(BaseModel):
priority: Literal["low", "medium", "high"]
sentiment: str
category: str
summary: str
next_action: strVector Databases & Embeddings
Learn how text becomes numerical vectors and how semantic similarity lets an application retrieve relevant information even when the query does not use the exact same words as the source document.
Semantic search demo
Learn
- Dense embeddings
- Dimensions and vector representations
- Cosine similarity and distance
- Nearest-neighbor search
- ANN at a high level
- Metadata and document IDs
- Chunk size and overlap trade-offs
Practical task
Create a small collection of text documents, generate embeddings, store them in ChromaDB or FAISS, and implement a function that returns the top relevant chunks for a natural-language query.
Retrieval-Augmented Generation (RAG) Systems
Combine retrieval with generation so the model can answer using information from your own documents. The important skill is understanding the complete data flow, not memorizing a framework API.
PDF question-answering RAG
Learn
- Document ingestion
- Parsing and cleaning
- Chunking and overlap
- Embedding generation
- Retrieval top-k
- Context construction
- Grounded generation
- Citations and source metadata
- Basic RAG failure modes
Practical task
Implement an end-to-end RAG pipeline from scratch: extract PDF text, split it into chunks, store embeddings in ChromaDB, retrieve the top three relevant chunks, then pass them to an LLM with an instruction to answer only from the supplied context.
Building Autonomous AI Agents (ReAct Loop)
Learn the difference between a simple LLM call, a workflow and an agent. Then give a model tools and let it decide when a tool is needed, observe the result and continue toward the final answer.
Tool-calling ReAct agent
Learn
- Workflow vs. agent
- Tool definitions and schemas
- Tool calling
- Reason–act–observe loop
- State and conversation history
- Retries and stopping conditions
- Human approval for risky actions
- Basic agent evaluation and tracing
Practical task
Build a custom Python agent with at least two tools, such as a calculator and a weather/search function. Let the LLM decide when to call them, return the tool result to the model, and stop after a bounded number of iterations.
API Development & Containerization
Wrap your AI workflow in a web API and package it so the application can run consistently across machines. Keep the first deployment simple: one service, one clear endpoint, predictable configuration.
/chat API + Docker image
Learn
- HTTP and REST basics
- FastAPI routes and request models
- Pydantic validation
- Interactive OpenAPI docs
- Environment variables and secrets
- Dockerfile basics
- Images vs. containers
- Port mapping and logs
Practical task
Create a FastAPI backend with a POST /chat endpoint that accepts a user query, runs the RAG or agent workflow from earlier days, and returns the answer plus useful metadata. Add a Dockerfile, build the image and run the service locally.
docker build -t ai-assistant .
docker run --env-file .env -p 8000:8000 ai-assistant
# then open:
http://localhost:8000/docsCapstone Mini-Project: End-to-End AI Assistant
Bring everything together by building a Personal Knowledge Assistant. A user should be able to provide documents, retrieve relevant context, ask questions, and use selected tools through a clean API.
GitHub + Dockerized AI Assistant
Minimum feature set
- Upload or ingest a PDF/text document
- Chunk and embed the content
- Store vectors with source metadata
- Retrieve relevant context for a query
- Generate a grounded answer
- Use at least one safe tool
- Expose the workflow through FastAPI
- Run the system through Docker
Portfolio upgrades
- Source citations with page/chunk metadata
- Conversation history or explicit session state
- Retrieval filters and top-k controls
- Confidence or unsupported-answer handling
- Request/response logging
- Basic evaluation dataset
- Docker Compose for multiple services
- Clear README architecture diagram
ai-assistant/
├── app/
│ ├── api/
│ ├── rag/
│ ├── agents/
│ ├── tools/
│ ├── models/
│ └── main.py
├── data/
├── tests/
├── .env.example
├── Dockerfile
├── docker-compose.yml
├── requirements.txt
└── README.mdREADME checklist
- Problem statement and target user
- Architecture diagram
- Tech stack and why each component exists
- Local setup instructions
- Environment variable template
- Docker build/run instructions
- Example API requests
- Evaluation or test results
- Known limitations and next improvements
Architecture
How the final system fits together
PDF/text upload and user query.
Chunk → embed → vector search → context.
Decide whether retrieval or a tool is needed.
Generate a grounded response with source metadata.
FastAPI validates requests and returns structured output.
Docker packages the application for repeatable execution.
Free resource library
Keep these references after Day 7
LLMs & prompting
Embeddings & RAG
APIs & deployment
Daily execution
How to spend each day
60–90 min concepts and notes.
90–120 min code-along and experiments.
90–120 min build your own version.
30–45 min document, test and commit.
Rule: every day must leave behind code. Do not spend the entire day watching tutorials.
Interview preparation
Questions you should be able to answer
LLMs
What are tokens? What is a context window? Why does temperature change output? What is streaming? What affects latency and cost?
Prompting
When should you use few-shot examples? Why use structured outputs? How do you validate and recover from malformed model responses?
RAG
Why embeddings? How do chunking and top-k affect retrieval? What is reranking? How do you evaluate retrieval separately from generation?
Agents
What makes an agent different from a workflow? How does tool calling work? How do you prevent infinite loops and unsafe tool use?
FastAPI
How do request models work? What is OpenAPI? How would you stream an AI response? How do you manage configuration and secrets?
Docker
What is an image vs. container? What goes in a Dockerfile? How do environment variables and ports work? How do you inspect logs and rebuild an image?
Portfolio value
Turn the 7 days into resume evidence
What to publish
- GitHub repository with clean commits
- Architecture diagram
- README with setup and usage
- Example API requests and responses
- Docker instructions
- Short demo video or screenshots
- Known limitations and next steps
Resume framing
Describe what you actually built and measured. For example: “Built a containerized knowledge assistant using Python, embeddings, ChromaDB, RAG, tool-calling agents and FastAPI, with source-aware retrieval and a documented Docker deployment workflow.” Replace generic wording with your actual implementation details and measured results.
Pro-Tip for Success
Don't just read the code—clone the repositories, break parts of them intentionally, and fix them. Hands-on debugging is one of the fastest ways to understand how AI systems actually behave. Keep your own notes on failures, trade-offs, and why you chose each component.
Finish line
Learn → Build → Test → Containerize → Deploy
At the end of Day 7, you should have one coherent application and enough understanding to keep improving it. The next step is to choose a domain—finance, healthcare, developer tools, education, customer support, legal documents, or another area—and adapt the same architecture to a real problem.