The Complete Prompt Engineering for AI Bootcamp (2026)

This repository is the notebook companion for the coding portions of The Complete Prompt Engineering for AI Bootcamp (2026) on Udemy.

It is built around Jupyter notebooks, small example apps, prompt files, datasets, screenshots, and diagrams rather than a single installable Python package. At the time of writing, the repo contains 248 notebooks covering prompt engineering, OpenAI workflows, retrieval, agents, LangChain, LangGraph, evaluation, vision, and image generation.

What This Repo Is For

• Course students who want the code from the hands-on lectures
• Developers who want practical prompt engineering and LLM application examples
• Teams looking for reference notebooks for OpenAI APIs, RAG, agent patterns, and eval workflows

Important Context

This repository does not mirror every lecture one-to-one.

The full course includes non-coding lessons, platform walkthroughs, and conceptual material that live mainly in video. The repo is strongest for the coding-heavy sections of the curriculum, especially:

• OpenAI API workflows
• Advanced prompting techniques
• Retrieval, embeddings, and RAG
• Agent design and orchestration
• LangChain and LangGraph
• Prompt optimization and evaluations
• Vision and image-model projects

Recommended Starting Points

If you are new to the repo, pick a track below:

OpenAI Foundations

• Chat Completions vs Responses API
• Responses API & Messages
• Structured Outputs
• Tool Calling

Prompt Engineering Techniques

• Few-Shot Learning
• Chain of Thought
• Self-Consistency Sampling
• Prompt Optimization

Retrieval & RAG

• Introduction to Retrieval
• Understanding Vector Embeddings
• RAG with Vector Databases
• Hybrid Retrieval with SQL Filtering & Embeddings

Agents

• Multi-Source Customer Support Agent
• Introduction to OpenAI Agents SDK
• Blog Post Generator Agent

LangGraph

• Simple LangGraph through Time Travel

Evals & Optimization

• Social Media Posts
• Evaluation Metrics
• DSPy Primer

Repo Map

Area	Folders	What you'll find
Core prompting and OpenAI	openai_features_and_functionality/, advanced_text_model_techniques/	Responses API, structured outputs, tool calling, streaming, async requests, few-shot prompting, ReAct, self-consistency, prompt optimization
Retrieval and agents	retrieval_embeddings_and_vector_databases/, advanced_retrieval_techniques/, building_ai_agents/, agent_architectures/	Embeddings, hybrid retrieval, retriever evaluation, support agents, OpenAI Agents SDK, orchestration patterns
Framework deep dives	deep_dive_on_langchain/, deep_dive_on_langgraph/	LangChain chat models, LCEL, vectorstores, agents, LangGraph state, persistence, human-in-the-loop, RAG, streaming
Projects and evals	ai_text_model_projects/, prompt_optimization_and_evals/, evaluating_quality/	Blog generation, long-document summarization, transcription, text-to-speech, realtime voice, DSPy, SAMMO, eval metrics
Vision and image models	vision/, advanced_image_model_techniques/, ai_image_model_projects/, standard_image_model_practices/	Multimodal analysis, product descriptions, FLUX/FAL image workflows, older Stable Diffusion and DreamBooth projects, placeholder image-practices folder
Supporting assets	building_ai_agents/resources/, images/, docs/	Sample datasets, prompt files, screenshots, architecture diagrams, and internal planning or audit material

Running The Notebooks

Most notebooks are designed to be opened in Jupyter or Google Colab.

For local use, a minimal setup looks like this:

python -m venv .venv
source .venv/bin/activate
pip install jupyterlab
jupyter lab

Notes:

• Many notebooks install additional dependencies inline with %pip install
• There is no single repo-wide requirements.txt or pyproject.toml for every notebook
• Most notebooks expect OPENAI_API_KEY
• Many notebooks will prompt for missing credentials with getpass if they are not already set in your environment

Credentials And External Services

Do not commit API keys or secrets to git.

Most notebooks can be run with just OPENAI_API_KEY, but some sections need extra services:

• ANTHROPIC_API_KEY for some prompt-caching comparisons
• SUPABASE_URL and SUPABASE_KEY for pgvector examples
• LANGCHAIN_API_KEY and TAVILY_API_KEY for some agent and tracing workflows
• FAL_KEY for several FLUX image-generation notebooks
• Twilio plus ngrok for the realtime voice example in ai_text_model_projects/openai_realtime_example_with_twilio/
• Hugging Face or Google Cloud credentials for some older image-model project notebooks

Use environment variables or your preferred local secret manager. If you use 1Password CLI, inject secrets locally rather than storing them in tracked files.

Notes On Repo Coverage

Some folders are more complete than others:

• deep_dive_on_langgraph/ is a structured sequence and works well as a guided learning track
• deep_dive_on_langchain/ is richer as a library of concepts than as a strict linear path
• building_ai_agents/resources/ contains the datasets, prompts, and knowledge-base files used by the agent notebooks
• ai_text_model_projects/openai_realtime_example_with_twilio/ is a standalone example app with its own README and pinned requirements.txt
• standard_image_model_practices/ is currently just a placeholder and should not be treated as a populated section
• Several vision and image notebooks depend on external assets, uploads, or cloud credentials and are not fully self-contained after clone

For Contributors

If you are maintaining or updating the repo, the existing hygiene tools are still useful:

brew install gitleaks
pip install pre-commit
pre-commit install
gitleaks detect --source="." --report-path="gitleaks-report.json"

Suggested Learning Paths

If you want a more deliberate progression:

1. OpenAI and prompting: openai_features_and_functionality/ -> advanced_text_model_techniques/
2. Retrieval: retrieval_embeddings_and_vector_databases/ -> advanced_retrieval_techniques/
3. Agents: building_ai_agents/ -> agent_architectures/
4. Frameworks: deep_dive_on_langchain/ and deep_dive_on_langgraph/
5. Applied projects: ai_text_model_projects/, prompt_optimization_and_evals/, vision/, advanced_image_model_techniques/

If you are following the Udemy course, use the videos for the full teaching sequence and use this repo as the coding companion.