The AI Engineering Stack
AI Engineering focuses on applying Foundation Models—large-scale AI models pre-trained on vast datasets—into production applications. Unlike traditional Machine Learning, AI Engineering shifts focus from training model architectures to orchestrating inference, evaluating performance, and connecting models to external tools and context.
- Prompt Engineering: Structuring context and heuristics to elicit optimal behavior.
- RAG (Retrieval-Augmented Generation): Grounding generation in factual, external databases.
- Evaluation: Using perplexity, exact matching, and "LLM-as-a-Judge" to score outputs.
- Finetuning: Creating domain-specific models via Parameter-Efficient Finetuning (PEFT).
Traditional ML: Feature Engineering ➔ Model Selection ➔ Training loop ➔ Deployment.
AI Engineering: Prompt Design ➔ Context Engineering (RAG) ➔ System Evaluation ➔ (Optional) Finetuning/Optimization.
Generation & Sampling
Language models output a probability distribution (logits) for the next token in a sequence. Sampling techniques like Temperature, Top-K, and Top-P (Nucleus) manipulate this distribution before the final token is chosen, balancing creativity against determinism.
Retrieval-Augmented Generation
RAG grounds LLMs in external knowledge by injecting relevant documents directly into the prompt context at runtime, solving hallucination and knowledge cutoff issues.
VRAM Math & Quantization
Inference optimization requires understanding GPU memory economics. Model weights, K-V caches, and activations consume VRAM. Quantization (reducing precision from 16-bit to 8-bit or 4-bit) drastically cuts hardware requirements.
Agentic Workflows
An Agent is an LLM equipped with tools (functions) and a cyclic reasoning loop (like ReAct: Reason + Act). Instead of a single generation pass, the agent can search the web, execute code, observe the results, and refine its internal state before answering.