The AI Engineering Bootcamp — AI skill for Claude Code

🧑‍💻 What is [AI Engineering](https://maven.com/aimakerspace/ai-eng-bootcamp)?

AI Engineering refers to the industry-relevant skills that data science and engineering teams need to successfully **build, deploy, operate, and improve Large Language Model (LLM) applications in production environments**.

In practice, this requires understanding both prototyping and production deployments.

During the *prototyping* phase, Prompt Engineering, Retrieval Augmented Generation (RAG), Agents, and Fine-Tuning are all necessary tools to be able to understand and leverage. Prototyping includes:

1. Building RAG Applications
2. Building with Agent and Multi-Agent Frameworks
3. Fine-Tuning LLMs & Embedding Models
4. Deploying LLM Prototype Applications to Users

When *productionizing* LLM application prototypes, there are many important aspects ensuring helpful, harmless, honest, reliable, and scalable solutions for your customers or stakeholders. Productionizing includes:

1. Evaluating RAG and Agent Applications
2. Improving Search and Retrieval Pipelines for Production
3. Monitoring Production KPIs for LLM Applications
4. Setting up Inference Servers for LLMs and Embedding Models
5. Building LLM Applications with Scalable, Production-Grade Components

This bootcamp builds on our two previous courses, [LLM Engineering](https://maven.com/aimakerspace/llm-engineering) and [LLM Operations](https://maven.com/aimakerspace/llmops) 👇

• Large Language Model Engineering (LLM Engineering) refers to the emerging best-practices and tools for pretraining, post-training, and optimizing LLMs prior to production deployment. Pre- and post-training techniques include unsupervised pretraining, supervised fine-tuning, alignment, model merging, distillation, quantization. and others.

• Large Language Model Ops (LLM Ops, or LLMOps (as from [WandB](https://docs.wandb.ai/guides/prompts) and [a16z](https://a16z.com/emerging-architectures-for-llm-applications/))) refers to the emerging best-p