This review is going to be split in two by a date: November 30, 2022. The day before ChatGPT launched, AI engineering was a specific, relatively niche discipline: you trained models on task-specific datasets, evaluated them with task-specific metrics, deployed them behind APIs, and monitored them with MLOps tooling. It required real ML expertise. The overlap with software engineering was partial. There was a skill wall between "AI practitioners" and "application developers."
After November 30, that wall started crumbling. But let's start with the world before it fell.
The Pre-ChatGPT ML World
In early 2022, the dominant paradigm for production AI at most organizations was supervised learning on structured data. Classification models for fraud detection, churn prediction, credit scoring. Gradient-boosted trees (XGBoost, LightGBM) dominated tabular data. Deep learning was reserved for computer vision and NLP tasks that actually required it.
The MLOps tooling had matured considerably from the "everyone's doing it differently" chaos of 2019–2020. MLflow was the standard for experiment tracking. Feature stores (Feast, Tecton, Databricks Feature Store) had emerged as a solution to the training-serving skew problem. Model registries gave you versioning and stage management. The rough stack:
- Training: Python + scikit-learn / XGBoost / PyTorch on Databricks or SageMaker
- Experiment tracking: MLflow (or Weights & Biases for deep learning)
- Feature management: Feature store (Feast, Tecton, or custom)
- Model registry: MLflow Model Registry or SageMaker Model Registry
- Serving: FastAPI + Docker on ECS/Kubernetes, or SageMaker endpoints
- Monitoring: Evidently, Fiddler, Arize — model drift and data quality monitoring
The challenge wasn't building models — it was everything around them. Getting features consistently between training and serving (feature skew). Retraining when the model drifted. Explaining predictions to auditors in regulated industries. Managing model versions without breaking production. None of these problems are solved by a better algorithm; they're engineering problems.
RLHF and the Alignment Moment
If there was one ML concept that 2022 made famous before ChatGPT, it was RLHF — Reinforcement Learning from Human Feedback. OpenAI published "Learning to summarize from human feedback" in 2020 and InstructGPT in January 2022, describing how they fine-tuned GPT-3 using human preference data to create a dramatically more helpful and less toxic model.
RLHF was the missing piece between "large language model that outputs plausible text" and "assistant that follows instructions and seems aligned with human preferences." The core loop: generate outputs, have humans rank them, train a reward model on the rankings, use the reward model to fine-tune the LLM via reinforcement learning (PPO). Technically complex, compute-intensive, and dependent on quality human labeling — but clearly effective.
For AI engineers in 2022, RLHF felt like an interesting research technique with unclear production applicability. In retrospect, it was the key that unlocked the AI assistant era.
Stable Diffusion: Open-Source Generative AI Arrives
August 2022: Stability AI released Stable Diffusion openly — weights, training code, and all. Unlike DALL·E (closed API) and Midjourney (Discord-only), Stable Diffusion could be run locally. On a consumer GPU. By anyone.
The immediate effect on AI engineering: a wave of developers who had never trained a generative model suddenly had a powerful one they could fine-tune, modify, and build applications with. Dreambooth fine-tuning (personalized image generation from a few photos) became an overnight trend. ControlNet (guiding image generation with structural inputs) followed. The image generation ecosystem exploded with open-source tooling in ways that the closed API providers couldn't match.
The longer-term effect: it established a template for the open-source AI ecosystem. When Llama 1 was released in February 2023 and Llama 2 in July 2023, the community had already learned how to rapidly build on open weights — from Stable Diffusion's playbook.
GitHub Copilot: The First Mass-Market AI Coding Tool
GitHub Copilot GA launched in June 2022. By the end of 2022, it had 1 million+ paying users and had demonstrably changed how developers wrote code — not by replacing them, but by handling boilerplate, suggesting completions, and accelerating the tedious parts. For AI engineers specifically, Copilot was useful for exactly the things data engineers and ML engineers do a lot of: writing data preprocessing code, generating test cases, writing SQL.
More importantly, Copilot proved that LLM-powered developer tools could have real retention and real willingness-to-pay — not just demo appeal. This de-risked investment in AI-powered developer tooling and set the stage for the explosion of AI coding assistants in 2023–2024.
Then: November 30
ChatGPT launches. Within a week, it's clear this is different from every AI product demo before it. Not because the technology is radically different from what researchers had seen — it's InstructGPT plus RLHF plus a chat interface. But because the interface made the capability accessible to everyone. You didn't need to understand prompt engineering or model APIs. You just talked to it.
The interface change was the innovation. GPT-3 had existed since 2020. The Instruct versions had been available via API since early 2022. The difference was the chat interface. It turned a capability that required technical knowledge to access into something a non-technical person could use immediately. That's not a technical innovation — it's a product innovation. And it's what caused the earthquake that 2023 had to deal with.
The irony: at the end of 2022, most AI engineers were thinking about 2023 in terms of MLOps maturation, feature store adoption, and model monitoring. The RLHF fine-tuning work, the alignment research, the chat interface — all of it was visible in the literature and in conference talks. But the emergent effect of combining them and putting them in front of 100 million users was not predicted. It rarely is.
If you were an AI engineer at the end of 2022, the skills you spent the year developing — ML model lifecycle management, feature engineering, model monitoring — remained valuable. But starting in January 2023, you needed to add a new dimension to your toolkit: understanding large language models, prompt engineering, and the new architecture patterns (RAG, agents, fine-tuning pipelines) that came with them. The good news: the foundation of data engineering and MLOps transferred directly. The plumbing changed; the engineering judgment didn't.