2026年AI工程全栈技术图谱

The AI engineering master stack in 2026!

(bookmark this)

It covers ten layers from the model itself to running it safely in production.

1) Foundations set how the model represents input through tokens, embeddings, transformer, attention, context window, positional encoding, and mixture of experts.

2) Model behavior covers how a trained model responds through pretraining, post-training, sampling, temperature, reasoning models, multimodality, and test-time compute.

3) Prompt engineering shapes the output through the prompt alone using system prompts, few-shot, chain-of-thought, structured outputs, prompt caching, self-consistency, and meta-prompting.

4) Retrieval feeds the model data it was never trained on through chunking, vector databases, hybrid search, reranking, retrieval eval, query rewriting, and GraphRAG.

5) Agents let the model take actions instead of only answering through function calling, ReAct, planning, reflection, multi-agent, computer use, and human-in-the-loop.

6) Context engineering controls what fills the context window across steps through context management, compaction, memory, MCP, agent harness, just-in-time retrieval, and structured note-taking.

7) Fine-tuning changes the weights when prompting and context fall short through SFT, LoRA, RLHF, DPO, distillation, GRPO, and RLVR.

8) Inference optimization makes the model cheap and fast to serve through quantization, KV cache, batching, speculative decoding, vLLM serving, FlashAttention, and PagedAttention.

9) Evaluation measures whether the system is actually correct through benchmarks, LLM-as-judge, golden datasets, hallucination detection, regression tests, trajectory evaluation, and red teaming.

10) LLMOps and safety keep the system reliable and safe in production through observability, cost tracking, guardrails, PII redaction, feedback loops, prompt injection defense, and model routing.

This grid below is the overview, but each layer is deep enough to be its own field with dedicated tooling.

If you need specific resources, I wrote a detailed article that provides a structured learning roadmap for AI engineers in 2026.

It covers prompting, RAG, fine-tuning, agents, MCP, evals, and inference, with guidance on what to prioritize and in what order.

Read it below.