1. What is AI Engineering?
AI engineering is the work of turning model capability into real product capability. It sits between raw research and usable software: prompts, retrieval, tool calls, memory, evaluation, observability, and deployment all matter as much as model choice.
2. The Core Stack
The typical stack looks like this: model API or open model, orchestration layer, vector or search system, application backend, frontend, and monitoring/evaluation. A strong AI engineer understands how these pieces fail, not just how they look in a demo.
The AI Engineering Core Stack →
3. LLMs and Prompting
Start with how language models behave: context windows, token limits, hallucinations, prompt structure, system instructions, and output formatting. Good prompting is less about magic phrasing and more about reducing ambiguity.
4. Pre-trained Models
You call a model someone else trained. Compare OpenAI, Claude, Gemini, and open hosts on context length, knowledge cutoff, and cost — then keep the one that wins your eval.
5. The OpenAI API
Chat completions, token counting, max tokens, pricing, the playground, and when fine-tuning is the wrong fix.
6. Embeddings
Vectors for meaning: semantic search, classification, recommendations, and anomaly detection. One embedding model per index.
7. Vector Databases
Where those vectors live. Index them, filter by tenant, and run similarity search. Postgres is allowed to be enough.
8. RAG and Knowledge Systems
Once a model needs external knowledge, retrieval becomes essential. That means chunking, embeddings, ranking, context assembly, and knowing when search quality is the real bottleneck instead of the model.
9. Agents and Tool Use
Agent systems become useful when the model can call tools: search, databases, code execution, APIs, calendars, CRMs, or internal services. The challenge is not just wiring tools up, but constraining and validating how they are used.
10. Multimodal AI
Images, audio, and speech: vision, image generation, Whisper, and text-to-speech. Video is frames plus audio, not a raw upload.
11. AI Safety and Ethics
Prompt injection, privacy, bias, moderation, and adversarial tests. The model is not your authorization layer.
12. Evals and Observability
If you can’t evaluate quality, you can’t improve it confidently. AI engineers need evals for correctness, relevance, latency, cost, and safety — plus observability to inspect prompts, retrieved context, outputs, and failure patterns in production.
13. Shipping to Production
Production AI systems need caching, fallbacks, rate-limit handling, prompt versioning, feedback loops, and cost awareness. The job is not to make the smartest prototype — it is to make a system that remains reliable when users and edge cases show up.